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

summary_metabarlist() and gghill_rarefaction() functions. Contaminants and tag-jumps were identified using contaslayers() and tagjumpslayer(). Additional metabaR [2] functions were utilized for data visualization. Results The COI datasets analysis of 2017 and 2018 samples generated 4,105,266 and 22,798,502 reads from 12 and 48 traps, respectively (Table 1). From the complete filter analysis of the 2019 and 2020 salt trap solutions, 14,189,292 and 39,580,980 sequence reads were generated from 23 and 46 samples, respectively (Table 1), while the quarter filter analysis of the 2020 samples yielded 45,115,854 sequence reads (Table 1), including reads observed in the negative controls (e.g., PCR negative and Sequencing controls). Similarly, 16,814,552 sequence reads were obtained for the 2022 samples, including field negatives and laboratory controls. The number of exact sequence variants (ESVs) among collection years ranged from 664 to 10,583 and com- prised 35 and 673 species, respectively (Table 1). No positive correlation was observed between the number of generated reads and the molecular operational taxonomic units (MOTUs) identified in all datasets except 2017, sug- gesting the decided sequencing depth in most cases was enough to recover the sample diversity given the primer cocktails used (Fig 1). These results were also ratified by rarefaction curves reaching the plateau for all samples in every collection year, except some in 2017 (e.g., D2; Fig 2). The sample analysis for each dataset in their experimental context showed no amplifica-

tion issues during library preparations. However, it also highlighted a considerable amount of reads in PCR controls (e.g., 2019 dataset; Fig 3), although they did not represent any MOTUs (Fig 3), suggesting spurious amplifications or PCR artifacts. No inconsistencies in PCR ampli- fication were observed for any dataset across rows or columns when analyzing the samples in their experimental context, which suggest no primer dysfunctionality or bias during library preparations. No contamination signal was observed; consequently, no contaminants were identified in the datasets based on the negative controls used. IAS detection and bioinformatics pipeline The number of IAS detected varied between mBRAVE and MetaWorks, with the mBRAVE pipeline consistently picking up a lower number of identifications in most collections except for the 2019 collection (Table 1; Fig 4). When the number of matches between molecular and morphological detection were analyzed per bioinformatic pipeline, MetaWorks resulted in the highest number of coincidences (Fig 4; Table 2). Consequently, we focused on the species that matched both bioinformatic pipelines (all except Rhagoletis cerasi and Lymantria albescens only found by mBRAVE), considering them to be “bioinformatically validated” and used MetaWorks as our reference pipeline moving forward. Table 1. The number of raw sequence reads (Seq reads) that were generated per dataset analyzed in the present study, including Exact Sequence Variants (ESVs),

number of Phyla (Phylum), Orders (Order), Genera (Genus), species (Species), and invasive alien species (IAS) detected based on eDNA after bioinformatic analysis with a bootstrap cut-off of 0.97 (MW = MetaWorks, and mBRAVE). Dataset Seq reads ESVs Phylum Order Family Genus Species IAS (MW) IAS (mBRAVE) 2017 4,105,266 664 2 12 32 35 35 1 1 2018 22,798,502 10,583 17 67 251 557 673 3 3 2019 14,189,292 5,051 13 53 204 397 445 4 5 2020 39,580,980 4,780 13 42 180 406 466 5 4 2020 Q 45,115,854 4,177 13 44 185 404 451 5 2 2022 16,814,552 3,960 9 42 182 389 444 4 3 https://doi.org/10.1371/journal.pone.0290036.t001 PLOS ONE Unveiling invasive insect threats to plant biodiversity PLOS ONE | https://doi.org/10.1371/journal.pone.0290036 August 11, 2023 8 / 24