Unveiling invasive insect threats to plant biodiversity: Leveraging eDNA metabarcoding and saturated salt trap solutions for biosurveillance

effective under similar experimental conditions [4, 25]: (1) trimming (30bp from the front and end, with a resulting trim length of 450bp); (2) filtering (minimum quality value (QV) of 20qv, minimum length of 200bp, maximum bases with low QV [<20] limited to 25%, and maximum bases with ultra-low QV [<10] limited to 5%); (3) pre-clustering threshold: none, ID distance threshold: 3%, minimum OTU size: 5, and OTU threshold: 2%; (4) assembler minimum over- lap of 20bp and maximum substitutions of 5bp; (5) The resulting sequences from this protocol were compared to libraries assembled from BOLD (Barcode of Life Data System [5]) records and matched to a BIN (alphanumeric code representing a molecular operational taxonomic unit) when possible. Each BIN represents a putative species and includes accompanying taxo- nomic information for the included records when available [28]. In addition to mBRAVE, MetaWorks was used as an alternative analysis to investigate the presence of regulated species [19]. MetaWorks is a pipeline for analyzing High-Throughput Sequencing data and is beneficial for its ability to analyze different markers and resulting sta- tistical bootstrap confidence values. MetaWorks takes raw paired-end reads as input, pair reads, then trims primers using CUTADAPT. Following trimming, MetaWorks dereplicates, and denoises reads using VSEARCH, then clusters and assigns taxonomy. An RDP (ribosomal database classifier) v2.13 was used for taxonomic classification (https://github.com/ terrimporter/CO1Classifier), accessed April 14, 2022. The parameters for MetaWorks are as

follows: (1) pairing (min quality: 20; min overlap; 25, the max fraction of mismatches: 0.02; min fraction of overlap: 0.90); (2) trimming and filtering (min length: 200bp; error rate: 0.1; end quality score: 20,20; min adapter overlap: 3; max number of N’s: (3); primer trimming for previously used COI primer cocktails reported in Milia´n-Garcı´a et al., 2020 [4]. MetaWorks outputs a results table including Exact Sequence Variants (ESVs), sequences, taxonomic assignments, and bootstrap support values for each taxonomic rank (S2 Table). In order to determine if any regulated species were present in the samples, the outputs from mBRAVE and MetaWorks were analyzed. The species returned by mBRAVE and MetaWorks (equal to and above a bootstrap value of 0.97) were compared against the updated regulated species lists in RStudio (version 022.02.0+443). To visualize the spread of regulated species, interactive and static maps were created using the leaflet, mapview, html widgets, and webshot packages in R. As metabarcoding reference databases often do not include all species of interest, the amount of missing data in the reference databases used by mBRAVE and MetaWorks was assessed. The regulated species list was compared against the databases used by both pipelines. Since mBRAVE uses BOLD, genus-specific data was accessed from BOLD on May 31, 2022, in R. Each genus from the regulated species list was mined from BOLD, which allowed for the regulated species to be compared to each species from BOLD. Since MetaWorks uses an RDP

classifier [19] (https://github.com/terrimporter/CO1Classifier), the regulated species were compared to the training set used (accessed on May 19, 2022). The effectiveness of a pipeline for IAS detection was considered based on the absolute number of molecular detections that matched morphological conformation. The higher the number of matches, the higher the effi- cacy of the bioinformatic pipeline in generating validated results. To assess potential contamination and tag-jumps, metabaR [2] was used after initial bioin- formatic analysis with MetaWorks [19]. metabaR is an R package that evaluates data quality by attempting to identify molecular artifacts using taxonomic outputs from previous bioinfor- matic pipelines, sample information, and PCR attributes. Each sampling year was analyzed separately in metabaR using the ESVs from MetaWorks and plate diagrams to determine the location of positive and negative controls. All input files were created manually, except for the MOTUs file, which was populated using a custom script, where duplicates were removed as per metabaR instructions. Data quality was assessed using each collection year’s PLOS ONE Unveiling invasive insect threats to plant biodiversity PLOS ONE | https://doi.org/10.1371/journal.pone.0290036 August 11, 2023 7 / 24