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A re-analysis of the data in Sharkey et al.'s (2021) minimalist revision reveals that BINs do not deserve names, but BOLD Systems needs a stronger commitment to open science. | LitMetric

AI Article Synopsis

Article Abstract

Halting biodiversity decline is one of the most critical challenges for humanity, but monitoring biodiversity is hampered by taxonomic impediments. One impediment is the large number of undescribed species (here called "dark taxon impediment") whereas another is caused by the large number of superficial species descriptions, that can only be resolved by consulting type specimens ("superficial description impediment"). Recently, Sharkey et al. (2021) proposed to address the dark taxon impediment for Costa Rican braconid wasps by describing 403 species based on COI barcode clusters ("BINs") computed by BOLD Systems. More than 99% of the BINs (387 of 390) were converted into species by assigning binominal names (e.g. BIN "BOLD:ACM9419" becomes Bracon federicomatarritai) and adding a minimal diagnosis (consisting only of a consensus barcode for most species). We here show that many of Sharkey et al.'s species are unstable when the underlying data are analyzed using different species delimitation algorithms. Add the insufficiently informative diagnoses, and many of these species will become the next "superficial description impediment" for braconid taxonomy because they will have to be tested and redescribed after obtaining sufficient evidence for confidently delimiting species. We furthermore show that Sharkey et al.'s approach of using consensus barcodes as diagnoses is not functional because it cannot be applied consistently. Lastly, we reiterate that COI alone is not suitable for delimiting and describing species, and voice concerns over Sharkey et al.'s uncritical use of BINs because they are calculated by a proprietary algorithm (RESL) that uses a mixture of public and private data. We urge authors, reviewers and editors to maintain high standards in taxonomy by only publishing new species that are rigorously delimited with open-access tools and supported by publicly available evidence.

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Source
http://dx.doi.org/10.1111/cla.12489DOI Listing

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