Danijela Šantić

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.

Ichthyoplankton provide critical insight into spawning activity, recruitment potential, and environmental drivers shaping fish populations. Despite their ecological importance, broad-scale ichthyoplankton studies in the Adriatic Sea remain limited. We analysed ichthyoplankton assemblages across the eastern Adriatic Sea, precisely five study areas—west coast of Istrian peninsula (I), Kvarner and Kvarnerić (K), northern middle Dalmatia (Z), wider area of Jabuka Pit (J), and southern Dalmatia (D), during two consecutive summers (2019–2020), combining plankton sampling using WP2 with DNA barcoding to improve taxonomic resolution. A total of 1382 eggs and 407 larvae were identified, representing 20 families and 34 species. Pelagic taxa dominated the assemblage, with anchovy (Engraulidae) accounting for more than 80% of ichthyoplankton. Spatial patterns revealed consistently higher abundances in Kvarner and Kvarnerić, the wider Jabuka Pit area, and southern Dalmatia—regions characterized by enhanced productivity, retentive circulation, or upwelling. Alpha biodiversity indices indicated high interannual similarity, while ANOSIM and SIMPER analyses confirmed significant differences (p < 0.05) among regions, driven primarily by Engraulidae and secondarily by Bothidae, Pomacentridae, Scombridae, Serranidae, and Sparidae. Redundancy analysis showed that temperature and chlorophyll-a were key environmental drivers influencing both pelagic and coastal taxa. These findings provide essential baseline information on ichthyoplankton dynamics in the eastern Adriatic Sea and highlight key spawning and nursery areas relevant for fisheries management. This study also demonstrates the value of integrating DNA barcoding with ecological analyses to strengthen ichthyoplankton monitoring in a rapidly changing marine environment.

Background: Aerobic Anoxygenic Phototrophic (AAP) bacteria are an essential component of aquatic microbial communities and play an important role in carbon cycling due to their ability to supplement their chemoorganotrophic metabolism with light-derived energy. While most of the previous studies focused on abundance, species composition and seasonal changes of AAP bacteria, their affinity for the particle-attachment did not attract much attention. Similarly, it remains unclear whether the entire AAP community is phototrophically active. This study investigated the seasonal changes in the composition of free-living and particle-attached AAP bacteria in the central Adriatic Sea’s coastal waters using both DNA and RNA puf M amplicon gene sequencing in the particle-attached and the free-living fractions. Results: AAP bacterial abundance grew from 1.27 × 10 ⁴ cells mL ^-1 in winter to 8.30 × 10 ⁴ cells mL ^-1 in summer. The proportion of AAP bacteria was consistently higher in the particle-attached fraction, particularly in spring and summer. DNA and RNA puf M amplicon analyses revealed large differences in activity among the species forming the AAP communities. Additionally, DNA-based assessments underestimated the phototrophic activity of certain genera, demonstrating discrepancies between the gene presence and its functional activity. Conclusions: Our data demonstrated that the expression of phototrophic genes in AAP bacteria is not uniform and largely varies throughout seasons and fractions. The particle-attached fraction harboured more than twice as many active AAP bacteria as the free-living fraction, with seasonal shifts and lifestyle driving changes in the phototrophy gene expression. RNA and DNA libraries revealed discrepancies between total and active AAP bacterial communities, emphasizing the necessity of transcript-based approaches for accurately assessing photoheterotrophic activity in marine environments. The pronounced partitioning of AAP bacterial diversity and activity between free-living and particle-attached fractions indicated the ecological specialization of certain AAP lineages, which may have noteworthy implications for the consumption of particulate organic matter and, ultimately, carbon cycling in coastal waters.

This study investigated the effects of experimental warming on the major bacterial phylogenetic groups in the marine environment, focusing on their growth and grazing rates, phylogenetic community composition and grazing preferences. A series of growth and grazing experiments were performed in microcosms at a temperature increase of 3°C. The growth rates of all bacterial groups were enhanced by temperature increase. SAR11 and Roseobacter were particularly sensitive to temperature changes, while Gammaproteobacteria were the least affected. Protozoan grazing mainly affected fast-growing groups such as Roseobacter and Gammaproteobacteria, while slower-growing groups such as SAR11 were less affected. In most cases, the growth rates of the bacteria increased more with temperature than the grazing rates of predators. This indicates that temperature has a stronger influence on bacterial growth than on predator activity. At higher temperatures the grazing pressure of nanoflagellates (NF) on Roseobacter and Bacteroidetes increased while Gammaproteobacteria were almost exclusively grazed by ciliates (CIL). As a result, the dominant predator of certain groups and the composition of prey changed with temperature increase. The changes in bacterial community composition at higher temperatures were caused both by the direct effect of temperature on bacterial growth and by shifts in predator grazing preferences. Furthermore, our results suggest that bacterial growth and grazing behaviour of bacterial predators do not necessarily occur simultaneously at higher temperatures and highlight the importance of considering bacterial phylogenetic composition when predicting bacterial response to warming.

Background: Aerobic anoxygenic phototrophs are metabolically highly active, diverse and widespread polyphyletic members of bacterioplankton whose photoheterotrophic capabilities shifted the paradigm about simplicity of the microbial food chain. Despite their considerable contribution to the transformation of organic matter in marine environments, relatively little is still known about their community structure and ecology at fine-scale taxonomic resolution. Up to date, there is no comprehensive (i.e. qualitative and quantitative) analysis of their community composition in the Adriatic Sea. Results: Analysis was based on pufM gene metabarcoding and quantitative FISH-IR approach with the use of artificial neural network. Significant seasonality was observed with regards to absolute abundances (maximum average abundances in spring 2.136 ± 0.081 × 10⁴ cells mL⁻¹, minimum in summer 0.86 × 10⁴ cells mL⁻¹), FISH-IR groups (Roseobacter clade prevalent in autumn, other Alpha- and Gammaproteobacteria in summer) and pufM sequencing data agglomerated at genus-level. FISH-IR results revealed heterogeneity with the highest average relative contribution of AAPs assigned to Roseobacter clade (37.66%), followed by Gammaproteobacteria (35.25%) and general Alphaproteobacteria (31.15%). Community composition obtained via pufM sequencing was dominated by Gammaproteobacteria clade NOR5/OM60, specifically genus Luminiphilus, with numerous rare genera present in relative abundances below 1%. The use of artificial neural network connected this community to biotic (heterotrophic bacteria, HNA and LNA bacteria, Synechococcus, Prochlorococcus, picoeukaryotes, heterotrophic nanoflagellates, bacterial production) and abiotic environmental factors (temperature, salinity, chlorophyll a and nitrate, nitrite, ammonia, total nitrogen, silicate, and orthophosphate concentration). A type of neural network, neural gas analysis at order-, genus- and ASV-level, resulted in five distinct best matching units (representing particular environments) and revealed that high diversity was generally independent of temperature, salinity, and trophic status of the environment, indicating a potentially dissimilar behaviour of aerobic anoxygenic phototrophs compared to the general bacterioplankton. Conclusion: This research represents the first comprehensive analysis of aerobic anoxygenic phototrophs in the Adriatic Sea on a trophic gradient during a year-round period. This study is also one of the first reports of their genus-level ecology linked to biotic and abiotic environmental factors revealed by unsupervised neural network algorithm, paving the way for further research of substantial contribution of this important bacterial functional group to marine ecosystems.

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.

Artificial neural network analysis (ANN) is used to study the seasonal distribution of viruses and microbial food web (MFW) components in the open Adriatic Sea. The effect of viruses within the MFW is often overlooked, although viruses play an important role in microbial community dynamics. The results showed that the strongest influence is found in the nonlinear relationship between viruses and temperature. In addition, the algorithm showed that the number of viral populations in the P-limited open sea varies by season and according to the abundance of their main hosts, HB. A strong positive relationship between viruses and HB was found in more than 50% of the observed data. Moreover, this algorithm confirmed the association of the virus with the autotrophic part of the picoplankton and with heterotrophic nanoflagellates. The dynamics of the four resulting clusters, characterized by biological and environmental parameters, is described as a cyclic pattern in the water layer above the thermocline. Neural gas network analysis has been shown to be an excellent tool for describing changes in MFW components in the open Adriatic.