Meier Rudolf, Blaimer Bonnie B, Buenaventura Eliana, Hartop Emily, von Rintelen Thomas, Srivathsan Amrita, Yeo Darren
Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore, 117558, Singapore.
Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Center for Integrative Biodiversity Discovery, Invalidenstraße 43, Berlin, 10115, Germany.
Cladistics. 2022 Apr;38(2):264-275. doi: 10.1111/cla.12489. Epub 2021 Sep 6.
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.
遏制生物多样性丧失是人类面临的最关键挑战之一，但生物多样性监测受到分类学障碍的阻碍。一个障碍是大量未描述的物种（这里称为“暗分类单元障碍”），而另一个障碍是由大量表面的物种描述造成的，只有通过查阅模式标本才能解决（“表面描述障碍”）。最近，沙基等人（2021年）提议通过基于BOLD系统计算的细胞色素氧化酶亚基I（COI）条形码聚类（“BINs”）描述403个物种来解决哥斯达黎加茧蜂的暗分类单元障碍。超过99%的BINs（390个中的387个）通过赋予双名法名称（例如BIN“BOLD:ACM9419”变为费德里科·马塔里塔茧蜂）并添加最少的诊断信息（大多数物种仅由一致的条形码组成）而被转化为物种。我们在此表明，当使用不同的物种界定算法分析基础数据时，沙基等人描述的许多物种是不稳定的。再加上诊断信息不足，这些物种中的许多将成为茧蜂分类学的下一个“表面描述障碍”，因为在获得足够证据以自信地界定物种后，它们将不得不接受检验和重新描述。我们还表明，沙基等人使用一致条形码作为诊断的方法不起作用，因为它不能一致地应用。最后，我们重申仅靠COI不适合界定和描述物种，并对沙基等人不加批判地使用BINs表示担忧，因为它们是由一种使用公共和私人数据混合的专有算法（RESL）计算出来的。我们敦促作者、审稿人和编辑在分类学上保持高标准，只发表那些使用开放获取工具严格界定并得到公开可用证据支持的新物种。
