Sanda Skejić

This two-year study investigated the spatial and temporal dynamics of picoplankton communities in the oligotrophic central Adriatic Sea, focusing on their responses and adaptation to environmental variability at two stations differing in trophic status, and identifying the key environmental factors shaping their community structure. The results indicated that chlorophyll a concentration, salinity, and nitrite concentration were the main environmental drivers of community picoplankton dynamics and structure, highlighting the combined influence of trophic status and hydrographic conditions. In addition, salinity was negatively correlated with chlorophyll a concentration. The picoplankton community responded to changes in these environmental conditions either through an increase in abundance, particularly under elevated chlorophyll a concentration, or a reduction in the relative contribution and abundance of most community members in response to increased salinity. In contrast, the cyanobacterial genus Prochlorococcus did not exhibit a decline in abundance with increasing salinity. Thus, the contrasting responses may reflect niche partitioning along the salinity gradient rather than direct physiological effects of salinity. The bacterial community structure was dominated by low nucleic acid bacteria, while an increase in bacterial biomass accompanied by reduced bacterial cell production was observed. In addition, a decline in heterotrophic nanoflagellates abundance may indicated reduced grazing pressure on bacterial populations. Overall, the findings suggest a shift toward more efficient, low-activity microbial communities under changing environmental conditions, with important implications for microbial-mediated carbon cycling in the Adriatic Sea.

Monitoring phytoplankton diversity is essential for the conservation of marine ecosystems and the protection of human health. This study compares light microscopy (LM) and DNA metabarcoding (MB) for analysing phytoplankton community at a long-term station in the Central Adriatic Sea, representing the first application of metabarcoding in this region. Sampling was conducted in June 2024 using Niskin bottles and a 53 µm plankton net. Four genetic markers (COI, 18S V4, 18S V9, rbcL) were applied to bulk plankton DNA to broaden taxonomic coverage and assess their detection performance relative to LM. Among the molecular markers, rbcL detected the highest taxonomic richness and was particularly effective for diatom species detection. In contrast, 18S V4 and 18S V9 captured a broader spectrum of phytoplankton diversity, especially outperforming LM and the other two molecular markers in detecting a previously unrecognized but ecologically important phytoflagellate group. Species-level resolution within the taxonomically challenging genera Chaetoceros and Pseudo-nitzschia was considerably improved with metabarcoding. However, several taxa observed by LM, including certain coccolithophores and thecate dinoflagellates, were not detected by any of the molecular markers. Overall, the results show that methodological choices strongly influence biodiversity estimates and highlight the value of a complementary integrative approach in assessing phytoplankton diversity.

The marine diatoms Pseudo-nitzschia spp. are globally distributed primary producers, with certain species capable of producing neurotoxin domoic acid (DA), causing amnesic shellfish poisoning (ASP). This study investigates the toxicity and growth rates of Pseudo-nitzschia species isolated from aquaculture areas in the Central Adriatic Sea. A total of 54 strains from eight species were analysed, with strains originating from four different study sites. Growth rates and toxin production were examined across different growth phases in other species and strains. Most species, including P. allochrona, P. calliantha, P. delicatissima, P. fraudulenta, P. galaxiae, P. mannii, and P. multistriata, did not produce DA at any growth phase. The only species that showed toxin production, at 18 °C was P. pseudodelicatissima (0.0007–0.0250 pg cell⁻¹). Notably, the majority of P. pseudodelicatissima strains were toxic in the decay phase and some strains showed continuous toxin production throughout all growth phases. The highest growth rates for the analysed species were recorded in strains of P. delicatissima, which also exhibited the highest cell abundance (8.19 × 10⁵ cell mL⁻¹), followed by the species P. allochrona, P. mannii and P. pseudodelicatissima.

The marine diatom genus Pseudo-nitzschia comprises cosmopolitan phytoplankton species commonly present in the Adriatic Sea. Species within the genus Pseudo-nitzschia have been of significant concern because they produce domoic acid (DA), which can cause amnesic shellfish poisoning (ASP). In this study, we identified Pseudo-nitzschia species along the Central and Southeastern Adriatic Sea, where monthly sampling carried out from February 2022 to February 2024 allowed for comprehensive species documentation. Pseudo-nitzschia species cell cultures isolated from the study areas were morphologically and molecularly analysed. Morphological analyses were performed using a scanning electron microscope (FE-SEM/STEM), while molecular analyses were conducted, targeting the ITS1-5.8S-ITS2, LSU, and rbcL regions, to confirm species identity. This integrative approach led to the identification of eight species: Pseudo-nitzschia allochrona, Pseudo-nitzschia calliantha, Pseudo-nitzschia delicatissima, Pseudo-nitzschia fraudulenta, Pseudo-nitzschia mannii, Pseudo-nitzschia multistriata, Pseudo-nitzschia pseudodelicatissima, and Pseudo-nitzschia subfraudulenta. Our findings underscore the value of a combined approach for reliable species identification and contribute to the development of genetic sequence databases that support the advancement of next-generation methods such as metabarcoding. This research emphasises the importance of combined morphological and molecular methods for the differentiation of the cryptic and pseudo-cryptic Pseudo-nitzschia species.

Considering the role of phytoplankton in the functioning and health of marine systems, it is important to characterize its responses to a changing environment. The central Adriatic Sea, as a generally oligotrophic area, is a suitable environment to distinguish between regular fluctuations in phytoplankton and those caused by anthropogenic or climatic influences. This study provides a long-term perspective of phytoplankton assemblage in the central eastern Adriatic Sea, with 14 years of continuous time series data collected at two coastal and two offshore stations. The predominant phytoplankton groups were diatoms and phytoflagellates, but their proportion varied depending on the vicinity of the coast, as evidenced also by the distribution of chlorophyll a. In the coastal environment, the phytoplankton biomass was substantially higher, with a higher proportion of microphytoplankton, while small phytoplankton accounted for the majority of biomass in the offshore area. In addition, a decreasing trend in diatom abundance was observed in the coastal waters, while such trend was not so evident in the offshore area. Using a neural gas algorithm, five clusters were defined based on the contribution of the major groups. The observed increase in diversity, especially in dinoflagellates, which outnumber diatom taxa, could be a possible adaptation of dinoflagellates to the increased natural solar radiation in summer and the increased sea surface temperature.

In this communication, we present an innovative approach leveraging advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques, specifically the Non-Negative Matrix Factorization (NMF) method, to analyze downward and upward light spectra collected by Hyperspectral Ocean Color Radiometer (HyperOCR, HOCR) sensors in the water column. Our work focuses on the development of a robust and efficient tool for unraveling the structure and activities of natural microbial assemblages in the ocean. By applying the NMF method to HyperOCR data, we successfully extracted five spectral signatures, representing unique patterns in the data. These signatures were instrumental in predicting the abundances of various microbial components, including bacteria, heterotrophic nanoflagellates, and picoeukaryotes, showcasing the potential of ML and AI in advancing oceanographic studies. To validate these methods, the study area included a shallow coastal area under the influence of freshwater inflow and an open offshore area with a depth of 100 m. The study sites in coastal and offshore waters (Kaštela Bay and Stončica Vis, respectively) had significantly different hydrographic and microbiological characteristics. Kaštela Bay had lower temperatures and salinity than the site on Vis. We have demonstrated prediction of the structure of the microbial community through application of different AI and ML methods with specific HOCR sensors.

In this communication, we present an innovative approach leveraging advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques, specifically the Non-Negative Matrix Factorization (NMF) method, to analyze downward and upward light spectra collected by Hyperspectral Ocean Color Radiometer (HyperOCR, HOCR) sensors in the water column. Our work focuses on the development of a robust and efficient tool for unraveling the structure and activities of natural microbial assemblages in the ocean. By applying the NMF method to HyperOCR data, we successfully extracted five spectral signatures, representing unique patterns in the data. These signatures were instrumental in predicting the abundances of various microbial components, including bacteria, heterotrophic nanoflagellates, and picoeukaryotes, showcasing the potential of ML and AI in advancing oceanographic studies. To validate the methods, the study area included a shallow coastal area under the influence of freshwater inflow and an open offshore area with a depth of 100 m. The study sites in coastal and offshore waters (Kaštela Bay and Stončica Vis, respectively) had significantly different hydrographic and microbiological characteristics. Kaštela Bay had lower temperatures and salinity than the site on Vis. We have proved by application of different AI and ML methods with specific HOCR sensors prediction of the structure of the microbial community.

Marine diatoms of the genus Pseudo-nitzschia are widespread and occur in various environments. About half of the species described today have been shown to be toxic, producing the potent neurotoxin, domoic acid. The Pseudo-nitzschia species are a common component of the Adriatic phytoplankton community and are occasionally predominant. In the present study, the composition of the Pseudo-nitzschia species was studied in four distinct aquaculture areas from February 2022 to February 2023. These are the first results of a comprehensive study of Pseudo-nitzschia diversity combining morphological and molecular analyses. The integrated approach resulted in the confirmation of Pseudo-nitzschia brasiliana, Pseudo-nitzschia hasleana, Pseudo-nitzschia galaxiae, and Pseudo-nitzschia linea. The species, P. galaxiae, was the most frequently occurring, P. linea was found in all investigated areas, while P. hasleana and P. brasiliana were rare, and their distribution was limited mainly to Kaštela Bay. The findings of P. brasiliana and P. hasleana represent the first confirmation for the Adriatic Sea, while P. galaxiae and P. linea have not been previously reported along the eastern Adriatic coast.

Coccolithophores are unicellular marine algae belonging to the haptophyte group, characterized by the production of intricate calcium carbonate plates that adorn their cells and exhibit species-specific morphology. The Adriatic Sea has historically been the type locality of numerous phytoplankton species, including coccolithophores. However, since the initial description, many species have not been recorded during the frequent phytoplankton surveys in the Adriatic Sea. This was mainly because these surveys did not use electron microscopy, which is necessary for accurate species identification. In this study, we re-evaluate the coccolithophore species lists using historical records and compare them with recent surveys in the coastal and open waters of the Adriatic Sea. In light of changes in nomenclature resulting from clarification of the species’ life cycles, we update the taxonomic list of coccolithophore species occurring in the Adriatic.