7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The negative global impacts of invasive alien species (IAS) on biodiversity are second only to habitat loss. eDNA metabarcoding allows for a faster and more comprehensive evaluation of community species composition, with a higher taxonomic resolution and less taxonomic expertise required than traditional morphological-based biosurveillance. These advantages have positioned eDNA metabarcoding as the standard method for molecular-based detection of invasive alien species, where fast and accurate detectability allows prompt responses to mitigate their adverse effects. Here, eDNA metabarcoding is used for biosurveillance of invasive alien species regulated by Canada in high-risk areas with four main objectives: i) validate the effectiveness of eDNA metabarcoding of salt trap solutions as a molecular technique for IAS detection, ii) compare detection from DNA extracts obtained from filter quarters versus whole filters, iii) benchmark two different bioinformatic pipelines (MetaWorks and mBRAVE), and iv) compare canopy and ground level trapping. eDNA from up to five IAS ( Agrilus planipennis, Daktulosphaira vitifoliae, Lymantria dispar, Popillia japonica, and Trichoferus campestris) were successfully detected across years from 2017 to 2022 in southern Ontario, Canada, with successful morphological validation for all except Lymantria dispar and Trichoferus campestris. Analysis of filter quarters in contrast to whole filters was demonstrated to be insufficient for effective IAS detection in each sample. All IAS were detected in only one filter quarter, suggesting a patchy eDNA distribution on the filter. The MetaWorks and mBRAVE bioinformatics pipelines proved effective in identifying IAS, with MetaWorks yielding a higher success rate when comparing molecular and morphological identifications. Ground-level and canopy-level sampling showed differential IAS recovery rates based on the molecular detection, which also varied per collection year, with all found IAS detected at the canopy level in 2022 while only one ( Lymantria dispar) in 2020. The present study ratifies the efficacy and importance of eDNA-based detection in a regulatory context and the utility of adding eDNA metabarcoding of saturated salt trap solutions, a critical tool for IAS detection.

          Related collections

          Most cited references62

          • Record: found
          • Abstract: found
          • Article: not found

          Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

          The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            MultiQC: summarize analysis results for multiple tools and samples in a single report

            Motivation: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis. Results: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization. Availability and implementation: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info Contact: phil.ewels@scilifelab.se
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              bold: The Barcode of Life Data System (http://www.barcodinglife.org)

              The Barcode of Life Data System (bold) is an informatics workbench aiding the acquisition, storage, analysis and publication of DNA barcode records. By assembling molecular, morphological and distributional data, it bridges a traditional bioinformatics chasm. bold is freely available to any researcher with interests in DNA barcoding. By providing specialized services, it aids the assembly of records that meet the standards needed to gain BARCODE designation in the global sequence databases. Because of its web-based delivery and flexible data security model, it is also well positioned to support projects that involve broad research alliances. This paper provides a brief introduction to the key elements of bold, discusses their functional capabilities, and concludes by examining computational resources and future prospects.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 August 2023
                2023
                : 18
                : 8
                : e0290036
                Affiliations
                [1 ] Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada
                [2 ] Ecological and Regulatory (Ecoreg) Solutions Inc., Guelph, Guelph, Ontario, Canada
                University of Helsinki: Helsingin Yliopisto, FINLAND
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-6596-5063
                Article
                PONE-D-23-13964
                10.1371/journal.pone.0290036
                10420381
                37566591
                4181daf4-e2ef-4f69-8d60-bf32d96b166a
                © 2023 Milián-García et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 May 2023
                : 31 July 2023
                Page count
                Figures: 7, Tables: 2, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100009837, Canadian Food Inspection Agency;
                Award ID: FAP#2122-002
                UoG-BIO acknowledges the financial support of the Canadian Food Inspection Agency (CFIA). [FAP#2122-002 ] The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Ecology and Environmental Sciences
                Species Colonization
                Invasive Species
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Research and Analysis Methods
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Biology and Life Sciences
                Taxonomy
                Computer and Information Sciences
                Data Management
                Taxonomy
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Beetles
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Beetles
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Beetles
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Trees
                Custom metadata
                The raw data (FASTQ files) generated or analyzed during the current study was made publicly available in the Borealis Research Data Repository (2017 collection: https://doi.org/10.5683/SP2/UM6RYF, 2018 collection: https://doi.org/10.5683/SP2/HTSZNS, and 2019, 2020, and 2022 collections: https://doi.org/10.5683/SP3/AGUXKR). All images of the IAS specimens discovered in the 2022 collection have been deposited in the Barcode of Life Data Systems (BOLD): dx.doi.org/10.5883/DS-IAS2022.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content751

                Most referenced authors718