"Dark taxonomy": a new protocol for overcoming the taxonomic impediments for dark taxa and broadening the taxon base for biodiversity assessment

Abstract

We are entering the 6th mass extinction with little data for "dark taxa" although they comprise most species. Much of the neglect is due to the fact that conventional taxonomic methods struggle with handling thousands of specimens belonging to hundreds of species. We thus here propose a new strategy that we call "dark taxonomy." It addresses (1) taxonomic impediments, (2) lack of biodiversity baselines, (3) and low impact of revisionary research. Taxonomic impediments are reduced by carrying out revisions at small geographic scales to keep the number of specimens low. The risk of taxonomic error is kept low by delimiting species based on two types of data. We then show that dark taxonomy can yield important biodiversity baseline data because it uses samples obtained with biomonitoring traps. Lastly, we argue that the impact of research can be improved by publishing two manuscripts addressing different readerships. The principles of dark taxonomy are illustrated by our taxonomic treatment of Singapores fungus gnats (Mycetophilidae) based only on Malaise trap samples. We show that a first batch of specimens (N=1,454) contains 120 species, of which 115 are new to science thus reducing taxonomic impediments by increasing the number of described Oriental species by 25%. Species delimitation started with using DNA barcodes to estimate the number of MOTUs before "LIT" (Large-scale Integrative Taxonomy) was used to obtain the species boundaries for the 120 species by integrating morphological and molecular data. To test the taxonomic completeness of the revision, we then analyzed a second batch of 1,493 specimens and found that >97% belonged to the 120 species described based on the first batch. Indeed, the second batch only contained 18 new and rare MOTUs; i.e. our study suggests that a single revision can simultaneously yield the names for all important species and relevant biodiversity baseline data. Overall, we believe that "dark taxonomy" can quickly ready a large unknown taxon for biomonitoring.