Exploring the mesoscale connectivity of phytoplankton periodic assemblages' succession in northern Adriatic pelagic habitats

An appropriate model for phytoplankton distribution patterns is critical for understanding biogeochemical cycles and trophic interactions in the oceans and seas. Because phytoplankton dynamics in coastal waters are more complex due to shallow depth and proximity to land, more accurate models applied to the correct spatial and temporal scales are needed. Our study investigates the role of the atmosphere and hydrosphere in pelagic habitat by modelling phytoplankton assemblages at two Long Term Ecological Research sites in the northern Adriatic Sea using niche-forming environmental variables (wind, temperature, salinity, river discharge, rain, and water column stratification). To study the synchronization between the phytoplankton community and these environmental variables at the two LTER sites, we applied current linear and nonlinear numerical methods for ecological modelling. The aim was to use periodic and/or non-periodic properties of the environmental variables to classify the phytoplankton assemblages at one LTER site (Gulf of Trieste - Slovenia) and then predict them at another LTER site 100 km away (Gulf of Venice - Italy). We found that periodicity played a role in the explanatory and predictive power of the environmental variables and that it was more important than non-periodic events in defining the common structure of the two pelagic habitats. The non-linear classification functions of the neural networks further increased the predictive power of these variables. We observed partial synchronization of communities at the mesoscale and differences between the original and predicted assemblages under similar environmental conditions. We conclude that mesoscale connectivity plays an important role in phytoplankton communities in the northern Adriatic. However, the loss of periodicity of niche-forming variables due to more frequent extreme meteorological and hydrological events could loosen these connections and affect the temporal succession of phytoplankton assemblages.

Corrigendum: Evaluation of the rbcL marker for metabarcoding of marine diatoms and inference of population structure of selected genera

[This corrects the article DOI: 10.3389/fmicb.2023.1071379.].

Evaluation of the rbcL marker for metabarcoding of marine diatoms and inference of population structure of selected genera

Diatoms are one of the most important phytoplankton groups in the world's oceans. There are responsible for up to 40% of the photosynthetic activity in the Ocean, and they play an important role in the silicon and carbon cycles by decoupling carbon from atmospheric interactions through sinking and export. These processes are strongly influenced by the taxonomic composition of diatom assemblages. Traditionally, these have been assessed using microscopy, which in some cases is not reliable or reproducible. Next-generation sequencing enabled us to study diversity in a high-throughput manner and uncover new distribution patterns and diversity. However, phylogenetic markers used for this purpose, such as various 18S rDNA regions, are often insufficient because they cannot distinguish between some taxa. In this work, we demonstrate the performance of the chloroplast-encoded rbcL marker for metabarcoding marine diatoms compared to microscopy and 18S-V9 metabarcoding using a series of monthly samples from the Gulf of Trieste (GoT), northern Adriatic Sea. We demonstrate that rbcL is able to detect more taxa compared to 18S-V9 metabarcoding or microscopy, while the overall structure of the diatom assemblage was comparable to the other two methods with some variations, that were taxon dependent. In total, 6 new genera and 22 new diatom species for the study region were identified. We were able to spot misidentification of genera obtained with microscopy such as Pseudo-nitzschia galaxiae, which was mistaken for Cylindrotheca closterium, as well as genera that were completely overlooked, such as Minidiscus and several genera from the Cymatosiraceae family. Furthermore, on the example of two well-studied genera in the region, namely Chaetoceros and particularly Pseudo-nitzschia, we show how the rbcL method can be used to infer even deeper phylogenetic and ecologically significant differences at the species population level. Despite a very thorough community analysis obtained by rbcL the incompleteness of reference databases was still evident, and we shed light on possible improvements. Our work has further implications for studies dealing with taxa distribution and population structure, as well as carbon and silica flux models and networks.