Evidence map›Paper›PMID 41561880›Full record

ArticleZoological studies2025

Effectiveness of DNA Barcoding Libraries Boosted through Taxonomy: the Case of a Neglected Taxon within an Underexplored Region.

Zucco Zucco, Sara La Cava, Teresa Bonacci, Stefano Scalercio

Abstract read
In one paragraph

Article in Zoological studies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zucco ZuccoCouncil 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).
Sara La CavaCouncil 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).
Teresa BonacciDepartment of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Rende, Italy. E-mail: teresa.bonacci@unical.it (Bonacci).
Stefano ScalercioCouncil 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).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

BiodiversityMicrolepidopteraTaxonomic impediment

Identifiers

PMID41561880
PMCPMC12813611

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.