# Benchmarking three DNA metabarcoding technologies for efficient detection of non-native cerambycid beetles in trapping collections

Loïs Veillat, Stéphane Boyer, Marina Querejeta, Emmanuelle Magnoux, Alain Roques, Carlos Lopez-Vaamonde, Geraldine Roux

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Loïs Veillat, Stéphane Boyer, Marina Querejeta, Emmanuelle Magnoux, Alain Roques, et al.. Benchmarking three DNA metabarcoding technologies for efficient detection of non-native cerambycid beetles in trapping collections. NeoBiota, 2024, 96, pp.237-259. 10.3897/neobiota.96.130195. hal-05525169

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# Benchmarking three DNA metabarcoding technologies for efficient detection of non-native cerambycid beetles in trapping collections

Loïs Veillat¹, Stéphane Boyer², Marina Querejeta²,³, Emmanuelle Magnoux¹, Alain Roques¹, Carlos Lopez-Vaamonde¹,²* and Geraldine Roux¹,⁴*

1 INRAE, UR633, Zoologie Forestière, F-45075 Orléans, France

2 Institut de Recherche sur la Biologie de l'Insecte, UMR7261 CNRS - Université de Tours, Avenue Monge, 37200 Tours, France

3 UMR CNRS 7267, Ecologie et Biologie des Interactions, Université de Poitiers, 3 Rue Jacques Fort, 86073, Poitiers, France

4 Laboratoire Physiologie, Ecologie et Environnement P2E, Université d'Orléans, 1 rue de Chartres, B.P. 6759, 45067 Orléans cedex 2, France

Corresponding authors: Loïs Veillat (lois.veillat@outlook.com); Geraldine Roux (geraldine.roux@univ-orleans.fr)

OPEN ACCESS

Academic editor: Marcela Uliano-Silva

Received: 21 June 2024

Accepted: 22 October 2024

Published: 25 November 2024

**Citation:** Veillat L, Boyer S, Querejeta M, Magnoux E, Roques A, Lopez-Vaamonde C, Roux G (2024) Benchmarking three DNA metabarcoding technologies for efficient detection of non-native cerambycid beetles in trapping collections. NeoBiota 96: 237–259. https://doi.org/10.3897/neobiota.96.130195

**Copyright:** © Lois Veillat et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0).

## Abstract

Individual sorting and identification of thousands of insects collected in mass trapping biosurveillance programmes is a labour-intensive and time-consuming process. Metabarcoding allows the simultaneous identification of multiple individuals in a single mixed sample and has the potential to expedite this process. However, detecting all the species present in a bulk sample can be challenging, especially when under-represented non-native specimens are intercepted.

In this study, we quantified the effectiveness of DNA metabarcoding at detecting exotic species within six different mock communities of native and non-native species of European xylophagous cerambycid beetles. The main objective is to compare three different sequencing technologies (MinION, Illumina and IonTorrent) to evaluate which one is the most suitable in this context. Additionally, dry and wet (monopropylene glycol and water) collection methods were compared. Although not observing significant differences in the total number of species detected amongst the three sequencing technologies, the MinION detected a greater number of species in field-like samples. All three sequencing technologies achieved success in detecting and identifying closely-related species and species in low abundance. The capture method of insects in the field greatly influenced sample preservation and detection. Individuals captured in traps containing monopropylene and water had lower DNA concentration, leading to lower species detection rates compared to individuals killed using just an insecticide without any collection medium.

**Key words:** Alien, biological invasions, biosecurity, Cerambycidae, exotic, Illumina®, IonTorrent®, Oxford Nanopore®, xylophagous

## Introduction

The exponential increase in biological invasions that has been observed over the past decades is expected to persist (Seebens et al. 2021). This is primarily due to factors such as globalisation, tourism and global warming (Chown et al. 2015). Amongst the species introduced beyond their native range by human activities, insects are the most prevalent group (Seebens et al. 2018) and can cause a wide range of impacts. Non-native insects can affect native flora, fauna and ecosystems in various ways

<footnote>* These authors contributed equally to this work.</footnote>
(Kenis et al. 2009) and they can also transmit pathogens and diseases, thus threatening public health (Mazza et al. 2014). Economic implications are also to be considered since numerous invasive insects are important pests for agricultural crops and plantation forests, inducing huge management costs (Bradshaw et al. 2016).

Amongst these non-native insects, species associated with woody plants are increasingly dominating, accounting for 76.5% of all herbivore species newly recorded in Europe from 2000 to 2014, potentially because of the growing trade of ornamental plants and wooden packaging material transported in international cargo shipments (Aukema et al. 2010; Roques et al. 2016). One of these important families of xylophagous beetles is the long-horned Cerambycidae, with more than 200 species affecting forestry, horticulture and agriculture (Rossa and Goczał 2021), resulting in multimillion-dollar losses every year (Wang 2017). To detect potential new invasions of Cerambycids, biomonitoring programmes have been set up over large geographical areas with intensive trapping campaigns extending over several years (Roques et al. 2023; Mas et al. 2023). However, rapidly evolving trades lead to changes in trade routes and imported goods, which results in an increasing arrival of new non-native species. A high number of these species have not been previously reported as invaders; some are not considered to be pests in their native ranges and some could even be unknown to science (Seebens et al. 2018). As part of the European project HOMED (https://homed-project.eu/), 244 Cerambycid traps were set up across Europe (France, Italy, Spain, Switzerland, Portugal, Austria, England, Greece, Slovenia, Netherlands, Bulgaria, Czech Republic and Sweden), 38 in Asia (China, Siberia, Russia), 11 in North America (USA, Canada), five in the Caribbean (Martinique) and four in Australia, all baited with generic lures for simultaneous detection of multiple species (Roques et al. 2023). In such large-scale trapping campaigns, thousands of captured insects must be sorted and identified by expert taxonomists. This identification step is time-consuming and labour-intensive, thus limiting the rapid detection of non-native individuals amongst large numbers of native ones (Piper et al. 2019; Abeynayake et al. 2021; Chua et al. 2023). Yet, it is essential that those non-native species are detected as quickly as possible to allow their eradication before establishment and dispersal (Richardson et al. 2000; Blackburn et al. 2011; Giovani et al. 2020).

For insects, traditional DNA barcoding, using a short fragment of the Cytochrome Oxidase 1 (COI) gene, has truly become a universal tool to identify unknown specimens at species level regardless of sex or life stage (Hebert et al. 2003). Namely, DNA barcoding has been successfully used to accurately identify cerambycid pest species for biomonitoring (Hodgetts et al. 2016; Wu et al. 2017; Kelnarova et al. 2019; Javal et al. 2021). Despite its numerous advantages, individual DNA barcoding remains a laborious and time-consuming approach in the context of mass-trapped insects, as it requires individual sorting of thousands of specimens, tissue sampling (often legs), DNA extraction and amplification and finally sequencing of each sample individually. However, the recent application of high-throughput sequencing (HTS) technologies to DNA barcoding allows for the expedited production of thousands of DNA barcodes (deWaard et al. 2019; Srivathsan et al. 2021).

This metabarcoding approach generates a large number of short DNA sequences (reads), allowing the accurate identification of multiple species simultaneously from a single mixed sample (hereafter called “bulk”) (Liu et al. 2020), such as all the individual insects captured in a single biomonitoring trap. Moreover, compared to traditional morphological identification, metabarcoding offers a significant reduction in costs (Batovska et al. 2021), generally providing equivalent or better
detection and identifying a much wider spectrum of taxa (Elbrecht et al. 2017; Andújar et al. 2018). Using DNA as a proxy for species detection and considering sequence variation within and amongst taxa, metabarcoding approaches are, however, constrained by the completeness of the reference databases to accurately assign sequences to correctly identified taxa (Liu et al. 2020).

Although metabarcoding has several advantages, ensuring the accuracy of detections is crucial. Erroneous detections of pest species can have severe environmental and economic consequences (Batovska et al. 2021). Yet, metabarcoding approaches still suffer from methodological limitations that may make them unsuitable for rapid biosecurity detection (contaminations, unreliable quantification, incomplete databases, false positives etc.). One specific challenge is the time required to process samples, which can suffer long delays between capturing individuals and obtaining sequencing results. This is especially true when sampling sites are located far away from laboratories or when transporting samples requires specific permits for certain species. Additionally, when external providers are slow to sequence samples, the process is further delayed. These limitations can hinder biomonitoring projects and slow down the detection of potential invasive species. As a result, the implementation of measures to mitigate their impacts may also be delayed (Krehenwinkel et al. 2019; Eger et al. 2022). Despite these limitations, the Illumina MiSeq sequencing technology has been favoured due to its lower error rate and well-established bioinformatic procedures (Piper et al. 2019). Yet, Braukmann et al. (2019) demonstrated similar performance in sequence quality and insect species recovery using IonTorrent platforms (Ion Torrent PGM and Ion Torrent S5). As these machines are more affordable than the MiSeq, they can easily be purchased by individual laboratories. Hence, this technology is overall less often dependent on external providers and their sequencing delays.

Over the past few years, Oxford Nanopore Technologies® has released a very inexpensive portable sequencing platform, the MinION. This small sequencer can be connected via USB to a laptop to perform sequencing (Krehenwinkel et al. 2019) in the field and obtain sequencing data in real time conditions. Indeed, the MinION for a metabarcoding application offers the possibility of performing DNA sequencing of bulk samples directly on site without the need for transport or reliance on external sequencing providers. So far, although the MinION does not seem suitable for the characterisation of complex communities, it is already suitable for the analysis of metabarcoding data when the species diversity per sample is low and the target species are well represented in public databases (Ho et al. 2020). In addition, recent developments in Nanopore technology and base calling have reduced sequence error from 6% (Srivathsan et al. 2021) down to less than 1% (Srivathsan et al. 2024).

The primary objective of our study was to determine the most effective metabarcoding approach for the biosurveillance of Cerambycid wood-boring beetles. To achieve this, we compared the performance of three Next Generation Sequencing technologies: the portable Nanopore sequencer MinION, the Illumina MiSeq and the Ion GeneStudio S5 (IonTorrent®). Our evaluation focused on their ability to detect invasive species in different mock communities. Specifically, we assessed their accuracy in differentiating between closely-related cerambycid species and detecting low-abundance species in mixed-trap samples. Additionally, we analysed various metabarcoding primer pairs to evaluate their accuracy in species identification. Finally, we emphasised the significance of the field sampling protocol, particularly the trapping methods (dry versus monopropylene glycol) in species detection.
# Materials and methods

## Taxa sampling

Mock communities were constructed using 48 field-trapped specimens from different countries in Europe (France, Greece, Portugal, Spain), China (Beijing and Zhejiang Province) and the USA (Michigan) (Table 1), as part of a worldwide trapping experiment using multi-funnel traps baited with a generic attractant blend, including eight Cerambycid pheromones (see details of the blend composition and trapping methods in Roques et al. (2023)). Most of the specimens (36/48) were caught using α-cypermethrin insecticide (Storanet®, BASF Pflanzenschutz Deutschland, Germany) in the trap basins, of which the bottom had been replaced with a wire mesh to allow drainage and keep specimens dry (hereafter called the “dry” method). Other specimens (12/48) were captured using a 50:50 ratio of monopropylene glycol (MPG) and water (hereafter called the “wet” method). Cerambycides collected from field-traps were stored in ethanol 95% and kept at -20 °C until further processing. Two individuals were captured by hand (“hand collected” in Table 1) and pinned in collection boxes after capture. The date, country of collection, type of trap and the 48 specimens used in mock communities are detailed in Table 1.

## Mock community construction and DNA extraction

Six mock communities with varying species composition were assembled as follows:

### Test 1: Identifying closely-related species.

To assess the efficiency of the different sequencing technologies and primers to differentiate between sister species, bulks 1 and 2 were composed of congeneric species (Table 1). Two legs from each individual (one specimen per species) were collected and pooled to constitute the bulks. The whole set of legs was then ground using flame-sterilised metal pestles to limit the risk of contamination. DNA from the ground material was extracted using the DNeasy Blood and Tissue Kit (Qiagen) following the manufacturer's instructions. Two additional legs were taken from the same specimens to assess the quantity and quality (A260/280 and A260/230 ratios) of DNA for each specimen individually (Fig. 1a).

### Test 2: Detecting low abundance species.

Bulks 3 and 4 were composed of six species represented by heterogeneous DNA concentrations (Suppl. material 1: table S1) to assess the ability of the sequencing technologies and primers to detect species present in very low abundance. DNA of each individual (one specimen per species) was previously extracted using two legs that were ground as above and processed using the Qiagen DNeasy Blood and Tissue Kit. To construct bulks 3 and 4, individual DNA extracts were quantified using a fluorometer (Nanodrop™, Thermo Fisher Scientific) and mixed together according to their concentration to achieve the needed proportions of DNA for each individual (six individuals of different species ranging from 41% to 3% for Bulk 3 and six individuals of different species ranging from 50% to 0.5% for Bulk 4) (Table 1, Fig. 1b).
<table><caption>**Table 1.** Species, origin, date and condition of capture of the specimens used in the six bulks. Species names in bold correspond to exotic species. We consider specimens that have been captured on a different continent from their place of origin as exotic.</caption><thead><tr><th>Bulk</th><th>Species</th><th>Country of collection</th><th>Collection Year</th><th>Collection type</th></tr></thead><tbody><tr><td>1</td><td>Arhopalus ferus</td><td>Portugal</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>1</td><td>Arhopalus rusticus</td><td>France</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>1</td><td>Arhopalus syriacus</td><td>Portugal</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>1</td><td>Xylotrechus arvicola</td><td>Portugal</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>1</td><td><strong>Xylotrechus chinensis</strong></td><td>Greece</td><td>2019</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>1</td><td><strong>Xylotrechus stebbingi</strong></td><td>Greece</td><td>2019</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>1</td><td>Xylotrechus undulatus</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td>Monochamus galloprovincialis</td><td>Portugal</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td>Monochamus sutor</td><td>France</td><td>2019</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>2</td><td>Monochamus carolinensis</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td>Monochamus scutellatus</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td>Phymatodes amoenus</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td><strong>Phymatodes testaceus</strong></td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td>Phymatodes varius</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td>Phymatodes aereus</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>2</td><td><strong>Phymatodes dimidiatus</strong></td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>3</td><td>Pyrrhidium sanguineum</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>3</td><td><strong>Xylotrechus stebbingi</strong></td><td>Spain</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>3</td><td>Monochamus galloprovincialis</td><td>Spain</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>3</td><td><strong>Xylotrechus chinensis</strong></td><td>Greece</td><td>2019</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>3</td><td>Chlorophorus glabromaculatus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>3</td><td>Phymatodes testaceus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>4</td><td>Arhopalus ferus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>4</td><td>Monochamus sutor</td><td>France</td><td>2019</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>4</td><td>Aegomorphus francottei</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>4</td><td>Monochamus galloprovincialis</td><td>France</td><td>2018</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>4</td><td><strong>Xylotrechus stebbingi</strong></td><td>Spain</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>4</td><td><strong>Xylotrechus chinensis</strong></td><td>Greece</td><td>2019</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>5</td><td>Pyrrhidium sanguineum</td><td>France</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>5</td><td>Batocera rubus</td><td>China</td><td>2012</td><td>Hand collected</td></tr><tr><td>5</td><td>Cerambyx scolopii</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>5</td><td><strong>Cordylomera spinicornis</strong></td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>5</td><td>Leiopus femoratus</td><td>France</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>5</td><td>Leiopus nebulosus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>5</td><td>Pachyta bicuneata</td><td>China</td><td>1987</td><td>Hand collected</td></tr><tr><td>5</td><td>Stictoleptura cordigera</td><td>France</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Arhopalus rusticus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td><strong>Xylotrechus chinensis</strong></td><td>Greece</td><td>2019</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Plagionotus detritus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Plagionotus arcuatus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td><strong>Xylotrechus stebbingi</strong></td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Arhopalus syriacus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Arhopalus ferus</td><td>France</td><td>2020</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Xylotrechus colonus</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr><tr><td>6</td><td>Chlorophorus ruficornis</td><td>France</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Phymatodes testaceus</td><td>France</td><td>2021</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Prionus coriarius</td><td>France</td><td>2010</td><td>Cypermethrin insecticide (dry method)</td></tr><tr><td>6</td><td>Phymatodes amoenus</td><td>USA</td><td>2019</td><td>Monopropylene glycol (wet method)</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Overview of the DNA extraction protocol for tests 1 (identifying closely-related species) and 3 (mimicking field trap content on species composition) (a) and for test 2 (detecting low abundance species) (b).</figcaption></figure>

## Test 3: Mimicking field trap content on species composition.

Bulks 5 and 6 were built to reconstitute real trap contents by a collaborator involved in Cerambycidae trapping campaigns using multi-pheromonal traps (Roques et al. 2023). These bulks include individuals from a number of species native to Europe usually found in the traps deployed there, with the addition of non-native species that have either already been introduced or are at risk of being introduced in Europe (Bulk 5: 22 individuals of eight species, including one non-native (*Cordylorella spinicornis*); Bulk 6: 41 individuals of 12 species including two non-native ones (*Xylotrechus stebbingi* and *Xylotrechus chinensis*) (Table 1). The DNA was extracted following the same protocol as for bulks 1 and 2 where two legs were taken from each individual and ground together for DNA extraction (Fig. 1a).

## PCR amplification

All bulk samples were amplified with two pairs of primers internal to the commonly used barcode fragment: BF3/BR2 (called hereafter “B”) (CCHGAYATRGCHTTYCCHCG / TCDGGRTGNCCRAARAAYCA (Elbrecht and Leese 2017; Elbrecht et al. 2019), which generates a 458 bp amplicon that was used for all the technologies; and fwhF2/fwhR2n (called hereafter “F”) (GGDACWGGWTAACWGTWTAYCCHCC / GTRATWGCHCCDGCTARWACWGG), which generates a shorter 254 bp amplicon (Vamos et al. 2017) and was used for Illumina and MinION technologies only. Each PCR comprised 15.3 µl H₂O, 2.5 µl 10X PCR buffer, 2.5 µl dNTPs [1 mM], 1 µl of each primer [0.4 mM], 0.2 µl Dream Taq (Thermo Fisher Scientific), 0.5 µl Betaine [100 mM] and 2 µl DNA for a total of 25 µl per reaction. For both primer pairs, PCR was performed using the same programme: 95 °C for 5 min, 29 cycles of 95 °C for 30 s, 48 °C for 30 s and 72 °C for 50 s, followed by 72 °C for 5 min (Elbrecht et al. 2019). PCR products were then run on a 2% agarose gel stained with ethidium bromide and
visualised by UV transilluminator. The PCR products were then purified with the NucleoFast 96 PCR plate purification kit (Macherey-Nagel). Three PCR replicates were performed for the six bulks when using the MinION technology.

## Illumina® library preparation

A second ligation PCR was performed on the products of the first PCR to add Illumina® tags and adapters, prepared by ligating Nextera XT indices through an eight cycle PCR (with a modified PCR protocol). The second PCR was carried out with the same conditions as for the initial PCR. Reactions (25 µl) contained the following: 5 µl of template DNA (purified products from the first PCR), 1 µl of each primer [10 µM], 5 µl of 5X GoTaq (Promega) reaction buffer, 1 µl of MgCl₂ [25 mM], 1 µl of BSA [1 mg/ml], 0.5 µl of dNTPs [5 mM], 0.125 µl of GoTaq G2 Polymerase (Promega) and 10.375 µl of molecular-grade water to reach 25 µl. The PCR conditions were the same as for the first PCR, with eight cycles. The products of the second PCR were verified on a 2% agarose gel. PCR products were then equimolarly pooled into two different pools (one pool per primer pair used) and purified using the GeneJET Gel Extraction kit from an agarose gel, following the manufacturer's instructions. This library was sequenced in Illumina MiSeq using V3 chemistry (300 × 300 bp, 600 cycles) in the Sequencing Center within the Biozentrum of the Ludwig-Maximilian University in Munich (Germany).

## MinION library preparation

Libraries were prepared according to the Oxford Nanopore Technologies ® protocol: “PCR barcoding (96) amplicons (SQK-LSK110) (version: PBAC96_9114_v110_revF_10Nov2020)” with the following specifications. After the first PCR described above, the Nanopore PCR barcoding expansion Pack 1-96 (EXP-PBC096) was used to perform the second PCR to incorporate the Oxford Nanopore Technologies ® barcode sequences on the amplicons generated in the first PCR.

Reactions (50 µl) contained the following: 2 µl of template DNA (purified products from the first PCR), 0.5 µl of each primer [10 µM], 10 µl of 5X GoTaq (Promega) reaction buffer, 2 µl of MgCl₂ [25 mM], 2 µl of BSA [1 mg/ml], 2 µl of Q solution, 1 µl of dNTPs [5 mM], 0.3 µl of GoTaq G2 Polymerase (Promega) and 29.7 µl of molecular-grade water to reach 25 µl. The thermocycling conditions followed the manufacturer recommendations: 95 °C for 3 min, followed by 15 cycles of 95 °C for 15 s, 62 °C for 15 s and 65 °C for 30 s and 65 °C for 7 min.

Final PCR products were then quantified using Qubit and equimolarly pooled before being purified with Agencourt AMPure XP beads (Beckam Coutler). The final pool was then sequenced on the MinION sequencer (Mk1c; Oxford Nanopore Technologies ®, UK) using a R10.3 flowcell (MIN111) with 1331 pores available and the LSK110 ligation sequencing kit. The two replicates of bulk 6 using the MinION technology were of insufficient quality (Nanodrop) and were, therefore, removed from the analysis.

## IonTorrent® library preparation

For the production of the libraries, we started with 5 ng of DNA extract (Qubit measurement). The Nextflex Cellfree DNAseq kit (PerkinElmer) was used for the process. The quality of the libraries was assessed using Qubit (for quantification)
and Bioanalyzer (using the HighSensitivity kit from Agilent, for size verification). After quality control, each library was amplified by emulsion PCR on the Ion One Touch 2 instrument, with a concentration of 15 pg/µl. Subsequently, the libraries were sequenced on an Ion GeneStudio S5 system using a single-end sequencing protocol with a 300 bp read length. Sequencing was performed on an Ion 520 Chip by the GeT-BioPuces platform (Toulouse, France).

## False positive detections

False positive detections are considered to be the detection of a species within a bulk that was not initially present when the bulks were constructed. In order to estimate the representativeness of false positives within true positives in the bulks, the total number of reads assigned to false positive OTUs was reconciled and compared to the number of reads assigned to non-false positive detections. The number of false positives detected according to the different tested combinations is indicated in Suppl. material 1: table S2.

## Reference barcode dataset

A dataset was built using all the public sequences of all Cerambycidae species available in BOLD systems v.4 (Ratnasingham and Hebert 2007). It was then verified whether all 33 species present in the bulk samples were represented by at least one sequence in the database. Three species not previously included in the database were barcoded through Sanger sequencing on an ABI 3500 genetic analyser (Applied Biosystems) using the Big-Dye Terminator V3.1 sequencing kit (Applied Biosystems) and BF3/BR2 primer pair. They were subsequently added to our local database to ensure that they were represented by at least one barcode sequence. The number of sequences in the database for each species is shown in Suppl. material 1: table S1.

The final reference dataset is available from BOLD in the dataset DS-MINION (dx.doi.org/10.5883/DS-MINION) and includes one barcode per species together with the three newly-generated barcodes. Lab Sheet from the DS-MinION database is shown in Suppl. material 1: table S3.

## Illumina® data processing

The raw data were analysed using the FROGS v.4.0.1 pipeline, a standardised pipeline containing a set of tools that are used to process amplicon reads that have been produced from Illumina® sequencing (Escudié et al. 2018; Henrie et al. 2022). First, amplicons with a size between 408 and 508 for the BF3/BR2 primer pair and 204 and 304 for the fwhF2/fwhR2n primer pair were retained using the Pre-process tool. For this first step, paired-end reads are merged using VSEARCH (Rognes et al. 2016) with a mismatch rate set at 10%. Cutadapt (Martin 2011) is then used to remove sequences in which both primers are absent and to trim the primers. Sequence clustering was then performed using the SWARM algorithm (Mahé et al. 2014) with a maximum sequence difference set at d = 1 (--distance 1 parameter), as recommended by SWARM. Chimeric sequences were then removed with the Remove chimera tool relying on VSEARCH with the de novo UCHIME method (Edgar et al. 2011; Rognes et al. 2016). Sequences were aligned to the same
database used for the MinION and IonTorrent® data analysis. In order to remove all spurious detections, OTU detections with fewer than 10 reads were removed. In barcoding and metabarcoding studies of insects, the sequence similarity level for OTU identification usually ranges from 95% to 99% (e.g. Gibson et al. (2014); Zenker et al. (2016)). We calculated the best threshold value for our dataset by applying the function localMinima from the R package spider v.1.5.0 (Brown et al. 2012). Based on this analysis, we used a threshold of 98% to assign OTUs to species level with blastn+ (Camacho et al. 2009). The resulting OTU tables for Illumina F and Illumina B are provided in Suppl. material 1: tables S4, S5, respectively.

## MinION and IonTorrent® data processing

Bioinformatics analyses were performed on the Genotoul Bioinformatics Platform (INRAE, Toulouse, France). Basecalling and demultiplexing were performed for MinION data using Guppy v.6.1.7; ONT; high accuracy base calling mode; parameters: -c dna_r10.3_450bps_hac.cfg --min_qscore 5 --trim_barcodes. Then, for MinION and IonTorrent® data, we used the *msi* data processing pipeline v.0.3.6 (Eger et al. 2022) to reduce the error rate of the reads by polishing them after the basecalling step. Reads smaller than 40 bp were removed with cutadapt v.4.0 (Martin 2011). The size range was set between 408 bp and 508 bp for BF3/BR2 and between 204 bp and 304 bp for fwhF2/fwhR2n. The clustering step was carried out with ISONCLUST v.0.0.6.1 (Sahlin and Medvedev 2020; with parameters: --mapped_threshold 0.825 and --aligned_threshold 0.55) and a consensus sequence per cluster was generated using RACON v.1.5.0 (Vaser et al. 2017). The polished reads were then clustered at 97% sequence identity with CD-HIT v.4.8.1 (Fu et al. 2012) and a representative sequence from each cluster (centroid) was selected. The polished reads were then aligned to the local database with BLAST (BLASTn algorithm). The following parameters were used: -word_size 11 -perc_identity 95 -qcov_hsp_perc 98 -gapopen 0 -gapextend 2 -reward 1 -penalty 1 -max_target_seqs 100. Similarly, to the Illumina® data processing, OTU detections with less than 10 reads were removed. Finally, a taxonomic assignment was performed for each query using a Lowest Common Ancestor (LCA) approach with the bioinformatics package metabinkit (Chain et al. 2016; Eger et al. 2018; Kitson et al. 2019) with the following thresholds: 98% at species level, 97% at genus level, 95% at family level (Alberdi et al. 2018; Eger et al. 2022). The resulting OTU tables for MinION B, MinION F and IonTorrent B are provided in Suppl. material 1: tables S6–S8, respectively.

## Statistical analysis

A two-sample test of proportions was used to compare and assess the significance of the proportion of reads assigned to the species levels for MinION, Illumina and IonTorrent technologies using the “Social Science Statistics” website (https://www.socscistatistics.com/tests/anova/default2.aspx). The proportion of reads assigned to different taxonomic levels was calculated by summing the total reads from different bulk samples for each condition. To determine if the number of false positives was significantly different amongst the three technologies and the two primer pairs, we calculated the detection mean for each bulk under different conditions. We then performed an ANOVA test followed by a Tukey HSD test using the “Social Science Statistics” website. The Wilcoxon test, Exact Fisher’s test and standard deviation
were calculated in R v.4.3.2 (https://www.R-project.org/). Sensibility, which measures the ability to correctly identify true positives, was calculated using the following formula: true positives / (true positives + false negatives) and precision, which measures the ability to measure the proportion of correct detections, was calculated using the following formula: true positives / (true positive + false positives).

## Results

A total of 1,248,595 reads were sequenced with the MinION Nanopore® technology using the F primer pair, with an average of 78,037 (SD = 28,415) reads per sample. After quality filtering and removal of reads of incorrect size or insufficient quality, 1,113,844 (89.2%) reads were retained, with an average of 69,615 (SD = 25,508) reads per bulk. For the B primer pair, a total of 1,132,604 reads were sequenced, with an average of 62,922 (SD = 17,442) reads per sample. After quality filtering, a total of 948,832 (83.8%) reads were retained, with an average of 52,712 (SD = 14,512) reads per bulk (Table 2).

<table><caption>**Table 2.** Number of raw reads obtained after sequencing and after pre-processing steps according to sequencing technologies and primer pairs used.</caption><thead><tr><th>Technology</th><th>Primer pair</th><th>n_raw_reads</th><th>n_reads_post_filtering</th></tr></thead><tbody><tr><td rowspan="2">MinION</td><td>B</td><td>1,132,604</td><td>948,832</td></tr><tr><td>F</td><td>1,248,595</td><td>1,113,844</td></tr><tr><td rowspan="2">Illumina</td><td>B</td><td>1,549,894</td><td>1,025,637</td></tr><tr><td>F</td><td>2,383,028</td><td>1,686,058</td></tr><tr><td>IonTorrent</td><td>B</td><td>838,489</td><td>280,695</td></tr></tbody></table>

The Illumina® sequencing produced a total of 1,549,894 reads using the B primer pair, with an average of 258,316 (SD = 39,365) reads per bulk. After quality filtering, 1,025,637 (66.2%) reads were retained, with an average of 170,940 (SD = 69,961) reads per bulk. For the F primer pair, a total of 2,383,028 reads were sequenced, with an average of 397,171 (SD = 84,482) reads per bulk. After quality filtering, 1,686,058 (73.3%) reads were retained, with an average of 281,010 (SD = 112,512) reads per bulk (Table 2).

Regarding the IonTorrent® technology, 838,489 reads were sequenced, with an average of 139,748 (SD = 17,086) reads per bulk with the B primer pair. After quality filtering, 280,695 (33.5%) reads remain, with an average of 46,782 (SD = 5,025) reads per bulk (Table 2).

## Benchmarking of sequencing technologies

The MinION technology accurately identified 28 out of 48 specimens at the species level, Illumina® technology allowed specific identification of 27 specimens and IonTorrent® identified 24 specimens. The primer pair F enabled the specific identification of 27 specimens at species level, while the primer pair B enabled the identification of 31 specimens at species level. Illumina® F, Illumina® B and MinION B allowed for 25 species-level identifications across all bulk (sensibility = 0.52) and 24 for MinION F and for IonTorrent® B (sensibility = 0.50). This difference was not significant (Fisher's Exact Test, p = 1.00) (Fig. 2).
The number of reads assigned at the species level was significantly higher with Illumina® technology (p.value < 0.00001) compared to MinION. Nearly 97% of reads were assigned at the species level for the Illumina® F combination compared to 90% for the MinION F combination (p.value < 0.0001). As for primer pair BF3/BR2, over 87.3% of reads were assigned at the species level for Illumina®, followed by over 79.7% for MinION technology and over 77.2% for IonTorrent® technology (Fig. 3). The primer pair fwhF2/fwhR2n resulted in a significantly higher percentage of reads assigned at the species level (93.6%) (considering both Illumina® and MinION technologies) compared to the couple of primers B (81.4%) (considering all three technologies) (p. value < 0.00001). The summary of the number of reads is shown in Suppl. material 1: table S9a, while the percentage is shown in Suppl. material 1: table S9b. Both tables display data assigned to the taxonomic levels of species, genus, family or higher, for all conditions tested.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Upset plot showing the number of individuals detected at species level according to the three technologies (Illumina, MinION and IonTorrent), primer pairs (F=fwhF2/fwhR2n [254 bp] and B=BF3/BR2 [458 bp]) and technology-primer pair combinations tested.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Proportion of reads assigned to each taxonomic level for each combination of sequencing technology and pair of primers (F: fwhF2/fwhR2n; B: BF3/BR2).</figcaption></figure>
False positive detections (i.e. a species detected within a bulk that is not part of the bulk's initial composition) were observed regardless of the combination of primers and technology (Fig. 4). The precision (the ability to measure the proportion of correct detections) of the Illumina F and MinION F combinations are 0.625 and 0.667, respectively, while for the Illumina B, MinION B and IonTorrent B combinations, the precision is 0.806, 0.926 and 0.8 respectively. An average of 13.5 false positive OTUs were recorded for the primer pair fwhF2/fwhR2n, compared to an average of four false positive OTUs when using the primer pair BF3/BR2, the difference being significant here (p value = 0.00194). According to the technology used, but regardless of the primers, an average of ten, seven and six false positives were recorded for Illumina®, MinION and IonTorrent® technologies, respectively. There are no significant differences amongst the three sequencing technologies in terms of false positives.

## Mock community analysis

In total, 33 out of 48 individuals (68.8%) were detected at the species level by at least one experimental condition (Fig. 5).

Bulks 1 and 2 were assembled to compare the detection rates of closely-related species under different sequencing and primer conditions. Illumina® detected seven species out of 16 (43.75%), MinION also detected seven out of 16 (43.75%) and IonTorrent® detected six species out of 16 (37.5%). No significant differences were observed amongst the different methods used (Krusal-Wallis chi-squared = 2, df = 2, p value = 0.3679).

Metabarcoding of bulks 3 and 4 aimed at comparing the ability of different sequencing technologies to detect low abundance species in the traps. All sequencing technology/primer combinations allowed for the detection of minor species: *Phymatodes testaceus* with a presence of 3% in bulk 3 (relative amount of DNA in the mock community) and *Xylotrechus chinensis* with a percentage of 0.5% in bulk 4. However, some species (although not in minority in the bulks) were not detected in one or several tests (Fig. 5). In total, Illumina® was able to detect a higher number of individuals (11/12 individuals detected) compared to MinION (9/12) and IonTorrent® (9/12).

Regarding bulks mimicking the species composition in a field trap, MinION performed better to detect and identify specimens at the species level in Bulk 6

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Number of false positive detections at species level for each sequencing platform and primers used (F=fwhF2/fwhR2n [254 bp] and B=BF3/BR2 [458 bp]).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Heatmap comparing the identification of individuals present in bulk samples at the species level (green square) or the absence of detection at the species level (grey square) according to the sequencing technologies and primer pairs used (F=fwhF2/FwhR2n; B = BF3/BR2). Species names written in blue were collected using the wet method, those in green were collected using the dry method and those in dark red were hand-captured.</figcaption></figure>

(detecting 8/12 species (66.7%)) compared to Illumina® and Ion Torrent® technologies (5/12 species (41.7%)), whereas the same number of species was detected for Bulk 5 (4/6 (66.7%)) regardless of the technology used. Nevertheless, in bulk 5, the non-native species, *Cordylomera spinicornis* was detected only by Illumina B. For bulk 6, the non-native species *Xylotrechus chinensis* was detected by all three technologies and *Xylotrechus stebbingi* by MinION B only.

**Impact of capture and storage conditions on individual detection**

Our results demonstrate significant differences in the mean number of detections between samples that were collected using the “dry” method (α-cypermethrin insecticide) and the “wet” method (water-diluted propylene glycol) (Wilcoxon rank-sum test, W = 74.5, p value = 0.0006342) (Fig. 6A). Indeed, 75% (9/12) of specimens collected using the “wet” MPG trapping procedure were not detected by any of the sequencing technologies. Conversely, 88.2% (30/34) of those collected using the “dry” trapping procedure (based on α-cypermethrin insecticide) were detected at least once across all technologies.

Individuals captured using the “dry” method had higher DNA concentration (39 ng/µl on average (SD = 52.79)) than MPG trapped specimens (18.6 ng/µl on average (SD = 21.80)) (Wilcoxon rank-sum test, W = 123.5, p value = 0.04533) (Fig. 6B). The average A260/280 ratio was 1.9 for the “dry” method and 2 for the MPG method (Fig. 6C). However, the average A260/230 ratio of specimens trapped with the “dry” method (0.8) was higher than that of specimens captured with MPG (0.5) (Wilcoxon rank-sum test, W = 146, p value = 0.1502) (Fig. 6D).

**Discussion**

Rapid and precise detection of exotic insects is crucial to prevent the ecological and economic damage they can cause by invading new environments and disrupting local ecosystems.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Boxplots representing (A) the average number of detections according to the type of preservation used, (B) the natural log-arithm scale (base e) of the average DNA concentration according to the type of preservation used, (C) the A260/280 quality ratio according to the type of preservation used and (D) the A260/230 quality ratio according to the type of preservation used. The black dots represent the outlier values (values outside the whiskers). The bold line represents the average value, outlines of the boxes represent the first and third quartiles and the whiskers represent the range of the values outside the quartiles.</figcaption></figure>

## Benchmarking of sequencing technologies

A slightly higher number of individuals were detected and identified to species using MinION (28 specimens) compared to Illumina® (27 specimens) or IonTorrent® (24 specimens), although this difference is not significant. However, this result demonstrates that the sequencing error rates long attributed to the MinION did not impact detection rates, while allowing for the elimination of the long delays often required when sequencing is performed on other sequencing technologies (Piper et al. 2019). It must be considered that we worked on a single pair of primers (BF3/BR2) with the IonTorrent® technology, which may have reduced the number of identifications. More specifically, our results showed that the choice of primer pairs and the length of the amplicon generated led to contrasted results regarding taxonomic assignment. For example, only BF3/BR2 allowed the species-level identification of the invasive species *Xylotrechus stebbingi*. This difference may be due to the longer amplicon generated by this primer pair, which has more informative nucleotide sites to provide a reliable taxonomic assignment. By contrast, fwhF2/fwhR2n generated a significantly higher number of false positives than BF3/BR2 (Fig. 4). This may be because the amplicon generated by fwhF2/fwhR2n is smaller in size compared to BF3/BR2. As a result, any loss of genetic information is more likely to result in misidentification or false positives (Meusnier et al. 2008).

Regardless of the number of identified species, the Illumina® technology produced a higher percentage of reads allowing species-level identification compared to MinION or IonTorrent®. The detection of specimens at a higher taxonomic level (genus or family) can be explained by sequencing errors that produce reads with less than 98% identity to the reference database. These results confirm that Illumina has a lower sequencing error rate than Oxford Nanopore's MinION sequencer (Piper et al. 2019), although this did not impact the number of individuals identified to the species level.
The three technologies showed similar efficiency in detecting and identifying closely-related species. Moreover, the results show that all three sequencing technologies (regardless of the associated primer pairs) enabled the detection and identification of species whose DNA represented a very low percentage in the mock community (Fig. 5). This high resolution would allow for the detection of exotic species that are poorly represented in traps, which might otherwise go unnoticed. Thus, all three technologies appear suitable for detecting and identifying species present in low numbers in field traps, enabling effective monitoring.

## Impacts of capture and storage conditions on DNA conservation

Both the conditions of capture (wet versus dry methods) and storage (i.e. time lag between collection and lab processing) have an impact on DNA concentration and quality and subsequently on the rate of species detection (Piper et al. 2019). Thus, the number of species detected is highly variable between bulks 1 and 2, which can be explained by the capture methods used: ‘dry,’ where individuals were captured without preservative fluid (as is the case for the majority of detected individuals comprising bulk 1) and ‘wet,’ where individuals were preserved in 50% MPG until trap retrieval (as is the case for the majority of undetected individuals comprising bulk 2). For instance, the species *Phymatodes testaceus* was always detected (10 out of 10 assays) when dry specimens were present, even in low concentrations (3% in Bulk 3). On the other hand, wet specimens of *P.testaceus* were rarely detected (one detection out of five assays). Individuals captured using the MPG method had lower DNA concentration and presented significantly lower detection rates compared to individuals captured using the “dry” method. Ballare et al. (2019) also found that insects collected in propylene glycol traps produced lower quality ddRADseq assemblages than specimens collected by net sampling and directly transferred into 100% ethanol (EtOH) or by passive trapping followed by 100% EtOH storage before pinning. In contrast to this, Ferro and Park (2013) found that propylene glycol is an effective DNA preservative for molecular marker-based studies on Coleoptera species. However, in their study, insects were first killed and preserved in 100% ethanol before being stored in glycol, while in our study insects were killed directly in propylene glycol. The use of 100% ethanol as the initial killing agent may lead to better initial preservation of specimens compared to direct exposure to propylene glycol.

## False positives, negatives and unmatched OTUs

Despite the precautions taken, several false positives were detected in all tested conditions. The number of false positives was significantly higher with the primer pair fwhF2/fwhR2n, which generates a smaller size amplicon compared to BF3/BR2. Even though Illumina technology is known to have a lower sequencing error rate compared to MinION (Silvestre-Ryan and Holmes 2021), our study found 10 false positives generated by Illumina, while MinION produced seven false positives and IonTorrent produced six. The sensitivity of HTS technologies allows for the detection of very small amounts of DNA, thus detecting even the slightest cross-contamination between samples (Liu et al. 2020). These DNA contaminations may have occurred during sample collection in the field or in the laboratory through cross-contamination between samples from the same study.
The false negative detections for some individuals may primarily be explained by the highly heterogeneous DNA quality of the different sequenced individuals (Suppl. material 1: table S1). In fact, DNA quality can be impacted by numerous mainly abiotic factors (pH, UV radiation, temperature), degrading DNA quality in a matter of days or weeks (Strickler et al. 2015; Collins et al. 2018; Harrison et al. 2019). During field trapping using stationary traps, captured insects are sometimes exposed to such conditions (high temperatures in trap containers when exposed to the sun in summer, high humidity in the container during heavy rains etc...), which can greatly accelerate the speed of DNA degradation in captured individuals. Such degraded DNA is more difficult to amplify, thus generating false negatives, especially when attempting to detect insects in low abundance within a trap, such as an invasive species in the process of establishing (Preston et al. 2022). Another possible cause for the high number of false negatives is the bias induced by PCR, such as uneven amplification of the DNA of the different individuals present in one sample (Preston et al. 2022). To avoid potential bias arising from identification errors or missing species in the reference databases, we decided to work on a local and curated BLAST database. However, when target species are partially unknown, as is the case in field conditions, analyses must rely on public reference databases. Yet, out of the 35,000 known species of Cerambycidae to date, only 2,926 species (8.4%) are recorded in BOLD with a barcode fragment (as of 16 November 2023). Furthermore, databases can contain errors such as mis-assignment of a DNA sequence to a wrong species due to morphological identification errors. This was precisely the error encountered for the species *Monochamus sutor*, which was genetically identified as *Monochamus sartor* (Suppl. material 1: table S7) or the species *Leiopus nebulosus* which has been genetically identified as *Leiopus linnei* (Suppl. material 1: table S7) using our local BOLD database.

One also needs to pay attention to synonymy whereby a species appears in the database under multiple names. We encountered this problem in our analysis with *Arhopalus ferus* (Bulks 1, 4 and 6) which was detected, but under the name of *Arhopalus tristis* (Suppl. material 1: table S10). Finally, mitochondrial paralogues such as NUMTs (non-functional copies of mitochondrial genes transported into the nuclear genome) present in databases can also bias results, making it impossible to identify specimens correctly at the species level (Bensasson et al. 2001). NUMTs are numerous in many organisms, including some cerambycids such as *Monochamus galloprovincialis* (Koutroumpa et al. 2009; Haran et al. 2015).

The differences in identification or non-detection between morphologically similar species belonging to the same genus, as observed, for example, with *Monochamus spp.*, *Phymatodes spp.* or *Arhopalus spp.* (Fig. 5), can be explained in part by the reasons mentioned above.

## Biases

Based on the results obtained, it appears that the main biases observed in metabarcoding analyses of trap contents stem from the degradation of DNA from individuals, which generates false negatives. We recommend favouring a “dry” rather than a “wet” trapping method, especially the MPG method and to plan for the collection, transportation and processing of captured individuals as soon as possible after capture. This includes checking the traps as frequently as possible (at least once a week), thus avoiding excessively long exposure of the individuals
to unfavourable environmental conditions. Once individuals are brought back
to the laboratory and if DNA cannot be extracted straight away, it is important
to limit any further degradation by keeping samples at -20 °C and in 95% eth-
anol. On the other hand, DNA extractions should be stored in the preservation
buffer provided with the extraction kits or in molecular-grade water and kept at
-20 °C (Preston et al. 2022). We also recommend limiting the use of primer pairs
that generate short amplicons, which can favour the amplification of non-target
taxa, NUMTs and lead to identification errors. The quality and completeness of
the databases are also very important bias factors. To limit this bias, Eger et al.
(2022) recommended restricting the database used to targeted species in order
to minimise the risk of false positives due to contamination. Limited taxonomic
and geographical coverage of sequence databases is a huge limitation in metabar-
coding studies. For example, Dopheide et al. (2019) found no representative se-
quence in the GenBank database for more than 900 invertebrate OTUs in their
study when analysing the community of soil arthropods from a native forest in
Ireland. Additionally, species identification errors and cases of synonymy lead to
false negatives or cases of multiple affiliations.

Conclusion

By comparing the accuracy and detection capacity of three metabarcoding strat-
egies, this study contributes to improving our toolkit for monitoring non-native
insect invasion. All three sequencing technologies performed equally well and
showed similar results for detecting and identifying exotic Cerambycid species col-
lected in field traps. However, MinION stands out as a portable, easy-to-use, and
cost-effective sequencer, with the potential to become an essential tool for biodi-
versity monitoring projects. Using MinION reduces the time spent on laboratory
handling compared to Illumina and eliminates the need to outsource sample se-
quencing. This saves considerable time when it comes to detecting invasive species.
The MinION technology is accurate enough to detect non-native species even
when present at low abundances in field traps and allows for accurate identifica-
tions as long as there is a sufficiently complete high-quality reference database to
avoid identification errors or false positives/negatives. It is also crucial to pay close
attention to issues of contamination and specimen preservation during and after
individual capture in order to work with the least degraded DNA possible.

Acknowledgements

We would like to thank all colleagues who participated in the taxa sampling (see
Roques et al. (2023)). We are thankful to Lucas Sire for insightful discussion on
primer choice. The authors thank the GeT-Biopuces platform of INSA Toulouse
for the IonTorrent sequencing study. We would like to thank the Mutualized
Platform for Environmental Genomics of the Institut de Recherche sur la Biologie
de l'Insecte, UMR 7261 CNRS, for access to its facilities and logistic support.

**Additional information**

Conflict of interest

The authors have declared that no competing interests exist.
## Ethical statement

No ethical statement was reported.

## Funding

This work was supported by the PORTRAP project "Test de l'efficacité de pièges génériques multi-composés pour la détection précoce d'insectes exotiques xylophages dans les sites potentiels d'entrée sur le territoire national" and HOMED project (HOlistic Management of Emerging Forest Pests and Diseases) which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 771271 (https://homed-projecteu/). We are grateful to the genotoul bioinformatics platform Toulouse Midi-Pyrenees for providing help and computing storage resources. Loïs Veillat was supported by a PhD studentship from HOMED project and doctoral school SSBCV at the University of Orléans.

## Author contributions

Loïs Veillat, Géraldine Roux, Carlos Lopez-Vaamonde and Stéphane Boyer conceived the study. Alain Roques collected field samples. Stéphane Boyer, Marina Querejeta, Emmanuelle Magnoux and Loïs Veillat conducted the laboratory sample processing. Loïs Veillat analysed the data and wrote the first draft. All authors contributed to the preparation of the manuscript. Both senior authors, Géraldine Roux and Carlos Lopez-Vaamonde, contributed equally to this study.

## Author ORCIDs

Loïs Veillat https://orcid.org/0009-0004-2149-1336

Stéphane Boyer https://orcid.org/0000-0002-0750-4864

Marina Querejeta https://orcid.org/0000-0003-1803-5239

Emmanuelle Magnoux https://orcid.org/0000-0003-0990-5511

Alain Roques https://orcid.org/0000-0002-3734-3918

Carlos Lopez-Vaamonde https://orcid.org/0000-0003-2278-2368

Geraldine Roux https://orcid.org/0000-0002-1116-2799

## Data availability

Barcode data for the 33 species used in the mock community experiment are available from BOLD in the dataset DS-MINION (dx.doi.org/10.5883/DS-MINION). Raw sequence data for this project and analytical script and files are available on figshare (https://figshare.com/projects/DNA_metabarcoding_an_efficient way_to_detect_non-native_cerambycid_beetles_in_trapping_collections_/_171432).

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**Supplementary material 1**

**Additional data (OTU tables, samples metadatas; summary tables; ...)**

Authors: Loïs Veillat, Stéphane Boyer, Marina Querejeta, Emmanuelle Magnoux, Alain Roques, Carlos Lopez-Vaamonde, Geraldine Roux

Data type: xlsx

Copyright notice: This dataset is made available under the Open Database License (http://opendata-commons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.

Link: https://doi.org/10.3897/neobiota.96.130195.suppl1