Mistaken identity: challenges with specimen identification for morphologically conservative skinks (Trachylepis) leads to taxonomic error

Abstract

ABSTRACT It is commonly recognised that natural history datasets contain locality errors that can compromise the utility of those datasets. However, another source of error in these datasets is taxonomic misidentifications, and this type of error is potentially common, particularly with regards to morphologically conservative species. For example, in the African skinks, the Trachylepis striata and T. varia species complexes each contain morphologically similar species that are commonly confused, despite being genetically distinct. Some species also are partly sympatric, and misidentifications are likely to be especially problematic in those areas. Using DNA barcoding, we assessed misidentification rates between species and applied the updated identifications to known distribution maps to examine whether those maps are accurate representations. Existing banked samples and newly collected samples were DNA barcoded using the mitochondrial 16S gene and supplemented with GenBank data. Identifications were made by matching sequences using haplotype networks that included material from near type localities. The barcode-based identifications were compared with the original identifications recorded for those samples. Taxonomic error was common, particularly in areas of presumed sympatry (error for T. striata species complex: 28%; T. varia species complex: 31%) and this resulted in inaccurately represented species distributions and areas of sympatry. Areas of sympatry were, however, confirmed for T. spilogaster/T. punctatissima, T. striata/T. punctatissima and T. damarana/T. laevigata/T. varia. Our findings corroborate other studies that demonstrate taxonomic error in existing datasets is a significant, but typically unrecognised problem, particularly for morphologically conservative species. This has implications for the utility of historical collections, citizen science records and public databases used in the formulation of species distribution maps, but also for other downstream analyses that rely on these datasets.