DNA METABARCODING FOR ZOOPLANKTON BIODIVERSITY ASSESSMENT THE CLASSICAL VS. THE MOLECULAR APPROACH

Zooplankton biodiversity assessment is a crucial element in monitoring marine ecosystem processes and community responses to environmental alterations. As morphological assessments of zooplankton assemblages are labour intensive, the characterization of the spatio-temporal variability would often require an unsustainable effort. The rising necessity for taxonomic information across trophic levels to support ecological research and ecosystem-based management efforts asks for fast and cost-effective methods for biodiversity assessment. Therefore, thanks to its advantage of increased sample processing speed and the broad taxonomic coverage, DNA metabarcoding is becoming an important tool for surveying marine biodiversity. It is a molecular approach for taxonomic identification, based on the sequencing of a DNA fragment, the barcode, that is unique to each species and can therefore be used for species discrimination. The present study aims to evaluate the suitability of DNA metabarcoding for zooplankton biodiversity assessment comparing it to the classical morphological method. Therefore, seasonal zooplankton sampling at six stations was carried out in 2016/17 in the Venice Lagoon and the nearby coastal area and each sample was analysed with morphological analysis and with molecular analysis. Compared to the morphological identification, DNA metabarcoding resulted in more taxa being resolved at species level (84% vs. 46% of assignments) and higher taxonomic richness (224 vs. 88 taxa), especially regarding mero-, ichthyoplankton and cryptic species. However, the ecological analysis resulted in very similar patterns comparing the two methods, certainly the molecular identification of the zooplankton community distinguished better between seasons. Especially in large-scale monitoring programs and biodiversity assessments of ecosystems with high spatial and temporal variability, where high sampling effort is required, DNA metabarcoding seems to be a promising tool.