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Competitive Genomic Screens of Barcoded Yeast Libraries
Published on: August 11, 2011
Effectiveness of DNA Barcoding Libraries Boosted through Taxonomy: the Case of a Neglected Taxon within an
Zucco Zucco1,2, Sara La Cava1,3, Teresa Bonacci3
1Council for Agricultural Research and Economics (CREA), Research Centre for Forestry and Wood, 87036 Rende, Italy. E-mail: giada.zucco@crea.gov.it (Zucco); sara.lacava@crea.gov.it (La Cava).
Abstract:
In recent decades, taxonomy has been significantly improved by integrating molecular techniques with classical morphological methods, leading to the discovery of cryptic species. On the other hand, molecular datasets by themselves are ineffective in several types of research without basic taxonomic studies, as the ecological and biological roles of a given species cannot be determined without an accurate name. DNA barcoding libraries are widely used as identification tools by non-specialists to overcome the taxonomic impediment, but they fail when basic taxonomic studies are insufficient and faunistic inventories are lacking. South European microlepidoptera are poorly studied, with the exception of a few families such as Depressariidae. We tested the effectiveness of the DNA barcoding library for this family to identify 174 specimens collected in southern Italy, where faunistic studies are very limited. All specimens were successfully barcoded, and 95% of them were assigned to 47 species, 43 of which correspond to a Barcode Index Number (BIN). Four additional species shared a BIN but were still clearly separated into different clusters at within-BIN resolution. Only seven specimens belonging to four BINs remain unnamed, and ad hoc studies are needed to clarify their status. The regional fauna was enriched by 37 species, three of which are new for the Italian mainland and 21 for peninsular Italy, demonstrating the usefulness of the DNA barcoding library in assessing local diversity and overcoming the taxonomic impediment. Improving taxonomic studies is crucial for utilizing molecular datasets to depict ongoing macroecological dynamics, highlight species richness trends, and identify changes in species assemblages.
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