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Developing a non-destructive metabarcoding protocol for detection of pest insects in bulk trap catches

  • 1.

    Bik, H. M. et al. Sequencing our way towards understanding global eukaryotic biodiversity. Trends Ecol. Evol. 27(4), 233–243 (2012).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 2.

    Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895 (2017).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 3.

    Porter, T. M. & Hajibabaei, M. Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis. Mol. Ecol. 27(2), 313–338 (2018).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 4.

    Arulandhu, A. J. et al. Development and validation of a multi-locus DNA metabarcoding method to identify endangered species in complex samples. GigaScience 6(10), gix080 (2017).

    Article 

    Google Scholar 

  • 5.

    Raclariu, A. C., Heinrich, M., Ichim, M. C. & de Boer, H. Benefits and limitations of DNA barcoding and metabarcoding in herbal product authentication. Phytochem. Anal. 29(2), 123–128 (2018).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 6.

    Staats, M. et al. Advances in DNA metabarcoding for food and wildlife forensic species identification. Anal. Bioanal. Chem. 408(17), 4615–4630 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 7.

    Comtet, T., Sandionigi, A., Viard, F. & Casiraghi, M. DNA (meta)barcoding of biological invasions: A powerful tool to elucidate invasion processes and help managing aliens. Biol. Invasions 17(3), 905–922 (2015).

    Article 

    Google Scholar 

  • 8.

    Piper, A. M. et al. Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience 8(8), giz092 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 9.

    Tedersoo, L., Drenkhan, R., Anslan, S., Morales-Rodriguez, C. & Cleary, M. High-throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations. Mol. Ecol. Resour. 19(1), 47–76 (2019).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 10.

    Andújar, C. et al. Metabarcoding of freshwater invertebrates to detect the effects of a pesticide spill. Mol. Ecol. 27(1), 146–166 (2018).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 11.

    Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8(10), 1265–1275 (2017).

    Article 

    Google Scholar 

  • 12.

    Brown, E. A., Chain, F. J. J., Zhan, A., MacIsaac, H. J. & Cristescu, M. E. Early detection of aquatic invaders using metabarcoding reveals a high number of non-indigenous species in Canadian ports. Divers. Distrib. 22(10), 1045–1059 (2016).

    Article 

    Google Scholar 

  • 13.

    Hebert, P. D. N., Ratnasingham, S. & deWaard, J. R. Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. B Biol. Sci. 270(Suppl 1), S96–S99 (2003).

    CAS 

    Google Scholar 

  • 14.

    Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270(15), 313–321 (2003).

    CAS 
    Article 

    Google Scholar 

  • 15.

    Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14(6), 1160–1170 (2014).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 16.

    Yu, D. W. et al. Biodiversity soup: Metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol. Evol. 3(4), 613–623 (2012).

    Article 

    Google Scholar 

  • 17.

    Brandon-Mong, G.-J. et al. DNA metabarcoding of insects and allies: An evaluation of primers and pipelines. Bull. Entomol. Res. 105(6), 717–727 (2015).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 18.

    Freeland, J. R. The importance of molecular markers and primer design when characterizing biodiversity from environmental DNA. Genome 60(4), 358–374 (2016).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 19.

    Marquina, D., Andersson, A. F. & Ronquist, F. New mitochondrial primers for metabarcoding of insects, designed and evaluated using in silico methods. Mol. Ecol. Resour. 19(1), 90–104 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 20.

    Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M. & Liebhold, A. M. Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecol. Lett. 15(8), 803–812 (2012).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 21.

    Batovska, J. et al. Effective mosquito and arbovirus surveillance using metabarcoding. Mol. Ecol. Resour. 18, 32–40 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 22.

    Liebhold, A. M. et al. Eradication of invading insect populations: From concepts to applications. Annu. Rev. Entomol. 61, 335–352 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 23.

    Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28(2), 420–430 (2019).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 24.

    Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10(7), e0130324 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 25.

    Krehenwinkel, H. et al. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Sci. Rep. 7(1), 17668 (2017).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 26.

    Piñol, J., Senar, M. A. & Symondson, W. O. C. The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol. Ecol. 28(2), 407–419 (2019).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 27.

    Ashfaq, M. & Hebert, P. D. N. DNA barcodes for bio-surveillance: Regulated and economically important arthropod plant pests. Genome 59(11), 933–945 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 28.

    De Barba, M. et al. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: Application to omnivorous diet. Mol. Ecol. Resour. 14(2), 306–323 (2014).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 29.

    Hauck, L. L., Weitemier, K. A., Penaluna, B. E., Garcia, T. S. & Cronn, R. Casting a broader net: Using microfluidic metagenomics to capture aquatic biodiversity data from diverse taxonomic targets. Environ. DNA 1(3), 251–267 (2019).

    Article 

    Google Scholar 

  • 30.

    Zhang, G. K., Chain, F. J. J., Abbott, C. L. & Cristescu, M. E. Metabarcoding using multiplexed markers increases species detection in complex zooplankton communities. Evol. Appl. 11(10), 1901–1914 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 31.

    Costello, M. et al. Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms. BMC Genomics 19(1), 332 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 32.

    MacConaill, L. E. et al. Unique, dual-indexed sequencing adapters with UMIs effectively eliminate index cross-talk and significantly improve sensitivity of massively parallel sequencing. BMC Genomics 19(1), 30 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 33.

    Bengtsson-Palme, J. et al. Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics 16(18), 2454–2460 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 34.

    Shen, Y.-Y., Chen, X. & Murphy, R. W. Assessing DNA barcoding as a tool for species identification and data quality control. PLoS ONE 8(2), e57125 (2013).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 35.

    Kozlov, A. M., Zhang, J., Yilmaz, P., Glöckner, F. O. & Stamatakis, A. Phylogeny-aware identification and correction of taxonomically mislabeled sequences. Nucleic Acids Res. 44(11), 5022–5033 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 36.

    Simmons, M., Tucker, A., Chadderton, W. L., Jerde, C. L. & Mahon, A. R. Active and passive environmental DNA surveillance of aquatic invasive species. Can. J. Fish. Aquat. Sci. 73(1), 76–83 (2015).

    Article 
    CAS 

    Google Scholar 

  • 37.

    Olmos, A. et al. High-throughput sequencing technologies for plant pest diagnosis: Challenges and opportunities. EPPO Bull. 48(2), 219–224 (2018).

    Article 

    Google Scholar 

  • 38.

    Darling, J. A., Pochon, X., Abbott, C. L., Inglis, G. J. & Zaiko, A. The risks of using molecular biodiversity data for incidental detection of species of concern. Divers. Distrib. 26(9), 1116–1121 (2020).

    Article 

    Google Scholar 

  • 39.

    Carew, M. E., Coleman, R. A. & Hoffmann, A. A. Can non-destructive DNA extraction of bulk invertebrate samples be used for metabarcoding?. PeerJ 6, e4980 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 40.

    Ji, Y. et al. SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes. Mol. Ecol. Resour. 20(1), 256–267 (2020).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 41.

    Nielsen, M., Gilbert, M. T. P., Pape, T. & Bohmann, K. A simplified DNA extraction protocol for unsorted bulk arthropod samples that maintains exoskeletal integrity. Environ. DNA 1(2), 144–154 (2019).

    Article 

    Google Scholar 

  • 42.

    Martins, F. M. S. et al. Have the cake and eat it: Optimizing nondestructive DNA metabarcoding of macroinvertebrate samples for freshwater biomonitoring. Mol. Ecol. Resour. 19(4), 863–876 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 43.

    Zizka, V. M. A., Leese, F., Peinert, B. & Geiger, M. F. DNA metabarcoding from sample fixative as a quick and voucher-preserving biodiversity assessment method. Genome 62(3), 122–136 (2018).

    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 

  • 44.

    Martoni, F., Valenzuela, I. & Blacket, M. J. Non-destructive DNA extractions from fly larvae (Diptera: Muscidae) enable molecular identification of species and enhance morphological features. Austral. Entomol. 58(4), 848–856 (2019).

    Article 

    Google Scholar 

  • 45.

    Plant Health Australia. Tomato-potato psyllid (2019). Retrieved 10 April, 2019 from http://www.planthealthaustralia.com.au/pests/tomatopotato-psyllid/.

  • 46.

    Yazdani, M. et al. First detection of Russian wheat aphid Diuraphis noxia Kurdjumov (Hemiptera: Aphididae) in Australia: A major threat to cereal production. Austral. Entomol. 57(4), 410–417 (2018).

    Article 

    Google Scholar 

  • 47.

    Pirtle, E., Maino, J., Lye, J., Umina, P., Heddle, T. & van Helden, M. Managing Russian wheat aphid risk—early season considerations. Centre for Environmental Stress and Adaptation Research (CESAR) (2019). Retrieved February 7, 2020 from http://www.cesaraustralia.com/assets/Uploads/PDFs/RWA-portal/Russian-wheat-aphid-green-bridge-surveillence-update-May-2019.pdf.

  • 48.

    Wilson, C., Rowbottom, R., Walker, P., Allen, G., Tegg, R. & Quarrell, S. Surveillance of tomato potato psyllid in the Eastern States and South Australia. Horticulture Innovation Australia (2018). Retrieved February 7, 2020 from https://ausveg.com.au/app/uploads/technical-insights/MT16016.pdf.

  • 49.

    Blackman, R. L. & Eastop, V. F. Aphids on the world’s crops: an identification and information guide. Aphids Worlds Crops Identif. Inf. Guide 2nd edn (2000).

  • 50.

    Kent, D. & Taylor, G. Two new species of Acizzia Crawford (Hemiptera: Psyllidae) from the Solanaceae with a potential new economic pest of eggplant, Solanum melongena. Aust. J. Entomol. 49(1), 73–81 (2010).

    Article 

    Google Scholar 

  • 51.

    Subcommittee on Plant Health Diagnostic Standards (SPHDS). Diagnostic protocol for the detection of the Tomato Potato Psyllid, Bactericera cockerelli (Šulc). Department of Agriculture, Australia (2017). Retrieved December 8, 2019 from https://www.plantbiosecuritydiagnostics.net.au/app/uploads/2018/11/NDP-20-Tomato-potato-psyllid-Bactericera-cockerelli-V1.2.pdf.

  • 52.

    Farrow, R. & Greenslade, P. Description of a robust interception trap for collecting airborne arthropods in climatically challenging regions. Antarct. Sci. 25(5), 657–662 (2013).

    ADS 
    Article 

    Google Scholar 

  • 53.

    Ferro, M. L. & Park, J.-S. Effect of propylene glycol concentration on mid-term DNA preservation of Coleoptera. Coleopt. Bull. 67(4), 581–586 (2013).

    Article 

    Google Scholar 

  • 54.

    Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 55.

    Martoni, F. Biodiversity, evolution and microbiome of the New Zealand Psylloidea (Hemiptera: Sternorrhyncha) (2017).

  • 56.

    Ouvrard, D., Campbell, B. C., Bourgoin, T. & Chan, K. L. 18S rRNA secondary structure and phylogenetic position of Peloridiidae (Insecta, hemiptera). Mol. Phylogenet. Evol. 16(3), 403–417 (2000).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 57.

    Kearse, M. et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28(12), 1647–1649 (2012).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 58.

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73(16), 5261–5267 (2007).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 59.

    Ratnasingham, S. & Hebert, P. D. N. BOLD: The barcode of life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7(3), 355–364 (2007).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 60.

    Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 37(Database issue), D26–D31 (2009).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 61.

    Chamberlain, S. bold: Interface to Bold Systems API. R package version 0.5.0 (2017). https://github.com/ropensci/bold.

  • 62.

    Winter, D. J. rentrez: An R package for the NCBI eUtils API. R J. 9(2), 520–526 (2017).

    Article 

    Google Scholar 

  • 63.

    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2014). http://www.R-project.org/.

  • 64.

    Sherrill-Mix, S. taxonomizr: Functions to Work with NCBI Accessions and Taxonomy. R package version 0.5.2 (2018). https://rdrr.io/cran/taxonomizr/.

  • 65.

    Mercier, C., Boyer, F., Bonin, A. & Coissac, E. SUMATRA and SUMACLUST: fast and exact comparison and clustering of sequences (2013). http://metabarcoding.org.

  • 66.

    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 67.

    Bushnell, B. BBMap short read aligner, and other bioinformatic tools (2017). https://sourceforge.net/projects/bbmap/.

  • 68.

    Ranwez, V., Douzery, E. J. P., Cambon, C., Chantret, N. & Delsuc, F. MACSE v2: Toolkit for the alignment of coding sequences accounting for frameshifts and stop codons. Mol. Biol. Evol. 35(10), 2582–2584 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 69.

    Saitoh, S. et al. A quantitative protocol for DNA metabarcoding of springtails (Collembola). Genome 59(9), 705–723 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 70.

    Wilcox, T. M. et al. Capture enrichment of aquatic environmental DNA: A first proof of concept. Mol. Ecol. Resour. 18(6), 1392–1401 (2018).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 71.

    McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), e61217 (2013).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 72.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

    Google Scholar 

  • 73.

    Walsh, P. S., Metzger, D. A. & Higuchi, R. Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10(4), 506–513 (1991).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 74.

    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35(6), 1547–1549 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 75.

    ABRS. Australian Faunal Directory. Australian Biological Resources Study, Canberra (2009). Retrieved October 30, 2019 from https://biodiversity.org.au/afd/mainchecklist.

  • 76.

    Bista, I. et al. Performance of amplicon and shotgun sequencing for accurate biomass estimation in invertebrate community samples. Mol. Ecol. Resour. 18, 1020–1103 (2018).

    CAS 
    Article 

    Google Scholar 

  • 77.

    Illumina. Effects of index misassignment on multiplexing and downstream analysis [White paper] (2017). Retrieved November 25, 2019 from https://www.illumina.com/content/dam/illumina-marketing/documents/products/whitepapers/index-hopping-white-paper-770-2017-004.pdf.

  • 78.

    Minich, J. J. et al. Quantifying and understanding well-to-well contamination in microbiome research. mSystems 4(4), e00186-19 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 79.

    Galan, M. et al. Metabarcoding for the parallel identification of several hundred predators and their prey: Application to bat species diet analysis. Mol. Ecol. Resour. 18(3), 474–489 (2018).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 80.

    Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 81.

    Meusnier, I. et al. A universal DNA mini-barcode for biodiversity analysis. BMC Genomics 9, 214 (2008).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 82.

    Elbrecht, V. & Steinke, D. Scaling up DNA metabarcoding for freshwater macrozoobenthos monitoring. Freshw. Biol. 64(2), 380–387 (2019).

    CAS 

    Google Scholar 

  • 83.

    Larsson, A. J. M., Stanley, G., Sinha, R., Weissman, I. L. & Sandberg, R. Computational correction of index switching in multiplexed sequencing libraries. Nat. Methods 15(5), 305–307 (2018).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 84.

    Gibbons, S. M., Duvallet, C. & Alm, E. J. Correcting for batch effects in case–control microbiome studies. PLoS Comput. Biol. 14(4), 1006102 (2018).

    ADS 
    Article 
    CAS 

    Google Scholar 

  • 85.

    Yeh, Y.-C., Needham, D. M., Sieradzki, E. T. & Fuhrman, J. A. Taxon disappearance from microbiome analysis reinforces the value of mock communities as a standard in every sequencing run. mSystems 3(3), e00023-18 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 86.

    McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. eLife 8, e46923 https://doi.org/10.7554/eLife.46923 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 87.

    Thomas, A. C., Deagle, B. E., Eveson, J. P., Harsch, C. H. & Trites, A. W. Quantitative DNA metabarcoding: Improved estimates of species proportional biomass using correction factors derived from control material. Mol. Ecol. Resour. 16(3), 714–726 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 88.

    Dowle, E. J., Pochon, X., Banks, C. & J., Shearer, K., and Wood, S.A. ,. Targeted gene enrichment and high-throughput sequencing for environmental biomonitoring: A case study using freshwater macroinvertebrates. Mol. Ecol. Resour. 16(5), 1240–1254 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 89.

    Peñalba, J. V. et al. Sequence capture using PCR-generated probes: A cost-effective method of targeted high-throughput sequencing for nonmodel organisms. Mol. Ecol. Resour. 14(5), 1000–1010 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 90.

    Liu, S. et al. Mitochondrial capture enriches mito-DNA 100 fold, enabling PCR-free mitogenomics biodiversity analysis. Mol. Ecol. Resour. 16(2), 470–479 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 91.

    Blackman, R. L. & Eastop, V. F. Aphids on the World’s Herbaceous Plants and Shrubs, 2 Volume Set (Wiley, 2008).

    Google Scholar 

  • 92.

    Edgar, R. C. Taxonomy annotation and guide tree errors in 16S rRNA databases. PeerJ 6, e5030 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 93.

    Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47(D1), D259–D264 (2019).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 94.

    Tang, C. Q. et al. The widely used small subunit 18S rDNA molecule greatly underestimates true diversity in biodiversity surveys of the meiofauna. Proc. Natl. Acad. Sci. U.S.A. 109(40), 16208–16212 (2012).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 95.

    Wangensteen, O. S., Palacín, C., Guardiola, M. & Turon, X. DNA metabarcoding of littoral hard-bottom communities: High diversity and database gaps revealed by two molecular markers. PeerJ 6, e4705 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 96.

    Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep. 8(1), 4226 (2018).

    ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 97.

    Porter, T. M. et al. Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier. Mol. Ecol. Resour. 14(5), 929–942 (2014).

    CAS 
    PubMed Central 
    Article 

    Google Scholar 

  • 98.

    Edgar, R. C. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. bioRxiv https://doi.org/10.1101/2020.05.12.088096 (2016).

    Article 

    Google Scholar 


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