Ecology of aerobic anoxygenic phototrophs on a fine-scale taxonomic resolution in Adriatic Sea unravelled by unsupervised neural network.

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.

Seawater physics and chemistry along the Med-SHIP transects in the Mediterranean Sea in 2016.

The Mediterranean Sea has been sampled irregularly by research vessels in the past, mostly by national expeditions in regional waters. To monitor the hydrographic, biogeochemical and circulation changes in the Mediterranean Sea, a systematic repeat oceanographic survey programme called Med-SHIP was recommended by the Mediterranean Science Commission (CIESM) in 2011, as part of the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP). Med-SHIP consists of zonal and meridional surveys with different frequencies, where comprehensive physical and biogeochemical properties are measured with the highest international standards. The first zonal survey was done in 2011 and repeated in 2018. In addition, a network of meridional (and other key) hydrographic sections were designed: the first cycle of these sections was completed in 2016, with three cruises funded by the EU project EUROFLEETS2. This paper presents the physical and chemical data of the meridional and key transects in the Western and Eastern Mediterranean Sea collected during those cruises.

Picoplankton diversity in an oligotrophic and high salinity environment in the central Adriatic Sea.

By combining qualitative 16S metabarcoding and quantitative CARD-FISH methods with neural gas analysis, different patterns of the picoplankton community were revealed at finer taxonomic levels in response to changing environmental conditions in the Adriatic Sea. We present the results of a one-year study carried out in an oligotrophic environment where increased salinity was recently observed. We have shown that the initial state of community structure changes according to environmental conditions and is expressed as qualitative and quantitative changes. A general pattern of increasing diversity under harsh environmental conditions, particularly under the influence of increasing salinity at the expense of community abundance was observed. Considering the trend of changing seawater characteristics due to climate change, this study helps in understanding a possible structural change in the microbial community of the Adriatic Sea that could affect higher levels of the marine food web.

Can Local Geographically Restricted Measurements Be Used to Recover Missing Geo-Spatial Data?

The experiments conducted on the wind data provided by the European Centre for Medium-range Weather Forecasts show that 1% of the data is sufficient to reconstruct the other 99% with an average amplitude error of less than 0.5 m/s and an average angular error of less than 5 degrees. In a nutshell, our method provides an approach where a portion of the data is used as a proxy to estimate the measurements over the entire domain based only on a few measurements. In our study, we compare several machine learning techniques, namely: linear regression, K-nearest neighbours, decision trees and a neural network, and investigate the impact of sensor placement on the quality of the reconstruction. While methods provide comparable results the results show that sensor placement plays an important role. Thus, we propose that intelligent location selection for sensor placement can be done using k-means, and show that this indeed leads to increase in accuracy as compared to random sensor placement.

Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea.

Bacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

The relationship between toxic phytoplankton species occurrence and environmental and meteorological factors along the Eastern Adriatic coast.

In this study, the time series of toxic phytoplankton species collected between 2004 and 2018 from the Northern Adriatic, Sibenik Bay, and Mali Ston Bay was analyzed in relation to environmental (temperature, salinity, water column stability, and river flow) and meteorological parameters (precipitation and wind). Because of the mostly non-linear relation between biotic and abiotic parameters, self-organizing maps (SOM) were used to identify these relationships. SOM analysis distinguished species of the genus Dinophysis from Gonyaulax spinifera and Lingulodinium polyedrum species, which better tolerate wind-induced disturbance. Among the Dinophysis species, Dinophysis fortii, Dinophysis tripos, and Dinophysis acuta preferred higher precipitation rate and river flow in addition to optimal temperatures. The abundances of Alexandrium species, which occurred more frequently in estuarine areas, were associated with river flow and maximum stable water column. Regardless of the ecological preferences of individual harmful algae, freshwater inflow-caused stratification is present in all clusters of environmental conditions associated with increased abundances of harmful algae in the SOM analysis. It is highly likely that stratification represents an important factor for the development and maintenance of HABs. The non-linear relationship between the NAO index and rainfall was noted, of which the most important for the development of harmful algae is the proportional correlation between the positive phase of the NAO index and higher rainfall, especially in winter and spring. Such conditions are conducive to the development of harmful algae because, with the increase in temperature accompanying the positive phase of the NAO index, increased rainfall further stimulates their growth. This can be achieved either through nutrient yields or through higher freshwater inflow that further stabilizes the water column.

Relations between mercury fractions and microbial community components in seawater under the presence and absence of probable phosphorus limitation conditions.

Microbial transformations of toxic monomethylmercury (MMHg) and dissolved gaseous mercury (DGM) at the lower levels of the marine food web are not well understood, especially in oligotrophic and phosphorus-limited seas. To examine the effects of probable phosphorus limitation (PP-limitation) on relations between mercury (Hg) fractions and microorganisms, we determined the total mercury (THg), total methylated mercury (MeHg), DGM, and microbiological and chemical parameters in the Central Adriatic Sea. Using statistical analysis, we assessed the potential microbial effects on Hg transformations and bioaccumulation. Only in the absence of PP-limitation conditions (NO-PP-limitation) is MeHg significantly related to most chemical and microbial parameters, indicating metabolism-dependent Hg transformations. The heterotrophic activity of low nucleic acid bacteria (abundant in oligotrophic regions) seems responsible for most of Hg methylation under NO-PP-limitation. Under these conditions, DGM is strongly related to microbial fractions and chlorophyll a, indicating biological DGM production, which is probably not metabolically induced, as most of these relations are also observed under PP-limitation. MMHg biomagnification was observed through an increased bioaccumulation factor from microseston to mesozooplankton. Our results indicate that Hg transformations and uptake might be enhanced under NO-PP-limitation conditions, emphasizing their impact on the transfer of Hg to higher trophic levels.

Oceanographic characteristics of the Adriatic Sea - Support to secondary HAOP spread through natural dispersal.

Vessels, specifically ballast water and hull fouling, are a major vector for the introduction of non-indigenous species (NIS) in European seas. The Mediterranean is one of the world's marine regions where their invasion is heaviest. The shallow Adriatic basin is a highly sensitive area that is already experiencing its consequences. The secondary spread of NIS over a wider area through natural dispersion is a complex process that depends on a wide range of oceanographic factors. This work analysed the dataset of the BALMAS project, in whose framework twelve ports in the Adriatic Sea were subjected to a Port Baseline Survey (PBS), to estimate the natural spread of NIS organisms from their port of arrival to the wider Adriatic basin. Its findings indicate that the prevailing water circulation patterns facilitate the natural dispersal of harmful aquatic organisms and pathogens (HAOP).