A tiered approach for high-resolution characterization of the soil faunal community via dna metabarcoding

Characterization of the huge soil faunal diversity still relies heavily on the slow and expertdependent morphological identification. This hampers our ecological understanding of spatial and temporal diversity of many faunal groups, as screening many samples at high taxonomic detail is not a realistic proposition. DNA-based approaches, especially high-throughput DNA metabarcoding assays, potentially solve this issue, but the development of such methods targeting soil fauna lags far behind that of soil microbes. Within the EU FP7-project EcoFINDERS, we developed and tested a framework for automated identification of six different groups of soil fauna at high taxonomic detail, with a single integrated method. We adopted a tiered approach, in which a general eukaryotic marker is used to screen for the presence of different eukaryotic clades and a set of more specific markers is simultaneously analyzed to obtain high resolution data for six different groups: mites, collembola, enchytraeids, nematodes, earthworms and protists. New primer sets, as well as reference barcode datasets were established for several of them. Here, we show the results of two test runs based on 454 pyrosequencing. In the first run, artificially created DNA pools of known composition were analysed to test to which extent the taxonomic composition could successfully be retrieved. Preliminary results show that for all groups the majority of species in the DNA pool were recovered by the metabarcoding approach. By comparing results for DNA pools that contained different relative amounts of DNA of the six groups, we could show that for most markers the number of taxa of the targeted group recovered depended on the presence of DNA from non-targeted groups. In the second run we moved towards the analysis of actual soil (e)DNA extracts, comparing the results of morphological identification by those of molecular identification based on the same soil samples.