Microscopy vs. metabarcoding: unravelling phytoplankton diversity in an eLTER coastal station in the Northern Adriatic Sea

The Northern Adriatic Sea is one of the most productive areas of the Mediterranean Sea. The combined effects of riverine inputs, circulation dynamics, shallow depths, and wind patterns renders it highly susceptible to climate change and ocean warming, with potential consequences on the phytoplankton communities. Long-term phytoplankton time series are crucial to detect tendencies and changes in the marine ecosystems, due to its high sensitiveness to environmental conditions, and to disentangle its variability, basic structure, phenology and regularity. The Senigallia-Susak Transect (SST) is a Long-Term Ecological Research marine site located in the southern part of the Northern Adriatic Sea, where phytoplankton abundances and biomass have been collected and analysed using the Utermöhl method since 1988. Moreover, samples for eDNA metabarcoding analyses have been collected since 2018 in the coastal station of the SST. In this study, we analyzed the long-term phytoplankton time series at the coastal station (SG01) of the SST to highlight the seasonal variability, the community structure, and the diversity of phytoplankton community. The results obtained using microscopy and eDNA metabarcoding were compared to highlight the strength and weakness of the two methods. DNA metabarcoding analysis was performed with different markers (18S rRNA V4 and V9) and using different reference databases (PR2 and SILVA), to evaluate the different combinations in the assessment of phytoplankton communities. Considering that metabarcoding measures the quantity of sequences and could overestimate taxa characterized by large genomes (e.g., dinoflagellates), the correction factors (CF) from Lapeyra Martin et al. (2022) were applied to relative abundances. Microscopy analysis revealed that phytoplankton annual maximum occurred in the winter months, mainly related to the diatom Skeletonema marinoi . Diatom blooms were also observed in spring and autumn with variable intensity. In periods between diatoms blooms, phytoplankton communities were dominated by heterogeneous communities of small phytoflagellates, while dinoflagellates increased only in spring-summer, with abundance peaks 1-2 orders of magnitude lower than those of diatoms. Coccolithophorids were a minor but persistent component of winter communities. The Indicator Value Analysis (IndVal) highlighted the taxa that were indicator of a certain season, such as Skeletonema marinoi in winter and Cerataulina pelagica in summer. The Graph-network analysis based on positive interactions showed that taxa not identified as seasonal indicators were the most important ones as they are not related to the particular conditions of a certain season, making them more able to interact inside the community. Comparing the diversity obtained by microscopy and metabarcoding, the latter revealed many genera and species never detected before in the study area, while microscopy detected taxa not revealed by metabarcoding. Metabarcoding allowed us to unravel the hidden biodiversity in groups only partially resolved by microscopy, such as dinoflagellates (which microscopy primarily identifies as naked or thecate) and phytoflagellates (generally identifiable only at the class level). However, metabarcoding failed to reveal almost all the coccolithophores, mainly due to the absence of their reference sequences in the databases. Only a small percentage of taxa was shared by: both methods (microscopy and metabarcoding), 18S regions (V4 and V9) and reference databases (PR2 and SILVA). both methods (microscopy and metabarcoding), 18S regions (V4 and V9) and reference databases (PR2 and SILVA). The use of PR2 as the reference database led to the highest number of identified phytoplankton genera and species, particularly with V4 as the marker, suggesting that this combination is, to date, the most effective for the assessment of phytoplankton diversity. Metabarcoding identified a community characterized by a higher number of phytoflagellate and dinoflagellate genera and species compared to microscopy, where diatoms and dinoflagellates were the most represented. Considering the relative abundances of the main phytoplankton groups, metabarcoding showed an overestimation of dinoflagellates compared to microscopy, where phytoflagellates were dominating. However, after applying CF, these differences were reduced. The results confirmed metabarcoding as a powerful tool, but it should be combined with microscopy to obtain more detailed information about the community and to counteract the drawbacks of metabarcoding, such as gaps in the reference databases and inability to provide values of absolute abundances.