The geographic and phylogenetic structure of public DNA barcode databases: an assessment using Chrysomelidae (leaf beetles) - Lo, Ellie; Nie, Rui-E; Vogler, Alfried P. (Frontiers in Ecology and Evolution, 2024)
DNA barcoding in insects has progressed rapidly, with the ultimate goal of a complete inventory of the world's species. However, the barcoding effort to date has been sporadic and leaves many areas of the world unsampled. This study investigates to what degree the current barcode data cover the species diversity across the globe, using the leaf beetle family Chrysomelidae as an example. A recent version (June 2023) of the Barcode-of-Life database included 73342 barcodes, grouped into 5310 unique BINs (species proxies) from 101 countries. Costa Rica contributed nearly half of all barcode sequences, while nearly 50 countries were represented by less than ten barcodes. Only five countries, Costa Rica, Canada, South Africa, Germany and Spain, had a high sampling completeness based on the barcode-to-BIN ratios and sampling coverage (SC) metrics. Phylogenetic placement of barcodes in a tree of ~600 mitochondrial genomes of Chrysomelidae showed a wide representation of barcodes across major taxonomic and biogeographically confined lineages. Measures of community phylogenetics using the Nearest Taxon Intervals (NTI) and related metrics showed that national faunas were clustered phylogenetically. This was supported by the finding that deeper local sampling did not greatly increase the phylogenetic diversity (PD) of the local community in the same way as it increased species diversity. However, at the species level the inventory remained incomplete even in the most intensely sampled countries, and the sampling generally remains insufficient for assessment of global species richness patterns. Despite the impressive growth of the barcode database over the past two decades, the current sequence-based inventory in Chrysomelidae (and probably most families of Coleoptera) needs to be greatly expanded to more areas and deeper local sampling before reaching a knowledge base similar to the existing Linnaean taxonomy. This study should help to prioritise target areas and to determine the required sampling depth for future barcoding efforts.