Comparison of Rapid Biodiversity Assessment of Meiobenthos Using MALDI-TOF MS and Metabarcoding - Rossel, Sven; Khodami, Sahar; Martínez Arbizu, Pedro (Frontiers in Marine Science, 2019)
Nowadays, most biodiversity assessments involving meiofauna are mainly carried out specimen by specimen based on morphological identifications, which is very time-consuming and demands comprehensive taxonomic knowledge. Specimens have to be examined for minor differences of setae compositions, mouthpart morphology or number of segments for various extremities. DNA-based methods such as metabarcoding as well as recently emerged rapid analyses using MALDI-TOF mass spectrometry to identify specimens based on a proteome fingerprint could vastly accelerate the process of specimen identification in biodiversity assessments. However, these techniques depend on reference libraries to connect assessed data to morphologically described species. In this study the success rate of both approaches have been tested based on reference libraries constructed using part of the samples from a new study area to identify unknown samples. Using MALDI-TOF MS we found, that species not contained in an incomplete mass spectra reference library only have minor impact on the results, when employing a post hoc test for Random Forest classifications. This test reveals specimens that demand morphological re-examination for the final species assignment. Metabarcoding however strongly demands a rich reference library to provide correct MOTU assessments in congruence with morphological determination. Nevertheless, with a complete library and a suitable data transformation (herein log(x+1)), the number or reads per MOTU reflects relative species abundances in metabarcoding inference. Furthermore, a single gene barcoding approach on selected specimens was performed here in order to provide a cost comparison among the three techniques. The results of this study facilitate specimen identification by using MALDI-TOF MS, which is incomparably cheap for specimen-by-specimen identification, but when it comes to sample wise analyses, metabarcoding outperforms other techniques by far.