In recent decades, the waters off the Antarctic Peninsula and surrounding region have undergone a significant transformation due to global climate change affecting the structure and distribution of pelagic fauna. Here, we present the results of our study on the taxonomic composition and quantitative distribution of plankton communities in Bransfield Strait, Antarctic Sound, the Powell Basin of the Weddell Sea, and the waters off the Antarctic Peninsula and South Orkney Islands during the austral summer of 2022. A slight warming of the Transitional Zonal Water with Weddell Sea influence (TWW) and an increase in its distribution area was detected. Among the pelagic communities, three groups were found to be the most abundant: copepods Calanoides acutus, Metridia gerlachei, and Oithona spp., salpa Salpa thompsoni, and Antarctic krill Euphausia superba. Euphausiids were found in cases of low abundance, species diversity, and biomass. In the studied region, an increase in the amount of the salpa S. thompsoni and the euphausiid Thysanoessa macrura and the expansion of their distribution area were observed. Significant structural shifts in phytoplankton communities manifested themselves in changes in the structure of the Antarctic krill forage base. The composition and distribution of pelagic fauna is affected by a combination of environmental abiotic factors, of which water temperature is the main one. The obtained results have allowed us to assume that a further increase in ocean temperature may lead to a reduction in the number and size of the Antarctic krill population and its successive replacement by salps and other euphausiids that are more resistant to temperature fluctuations and water desalination.

The study aimed to investigate krill (Euphausia superba) and salp (Salpa thompsoni) populations in the Atlantic sector of the Southern Ocean in January and February 2022. Samples were obtained to measure the abundance, biomass and distribution patterns of krill and salp. Sex differences and feeding habits of the Antarctic krill were determined. The dependence of the physiological state of the studied aquatic organisms on changes in environmental parameters was analyzed. Current data on the association of the dynamics of hydrometeorological parameters and processes with the distribution of chlorophyll a, krill, and salp were obtained. It was established that, at numerous stations, the biomass of salps prevailed over krill. The result indicates the replacement of the Antarctic krill populations by gelatinous zooplankton. The obtained results allow assessment of the biological resource potential in the studied region based on the analysis of the samples collected.

The present study is the attempt to combine oceanologic measurements and biochemical analysis, which is as possible to implement on board as in a laboratory with chosen certain statistics to reveal trophic conditions and the environment state in which Antarctic krill live in season in real time on site. The fatty acid constituents of total lipids in juvenile and mature Antarctic krill sampled from the Bransfield Strait (BS), the Antarctic Sound (AS), and waters at the eastern tip of the Antarctic Peninsula (AP) collected during the 87th cruise of the R/V Akademik Mstislav Keldysh in January–February 2022 were analyzed. The fatty acid (FA) profile in juvenile and mature Antarctic krill was studied by gas chromatography with a mass selective detector to identify the qualitative composition and a flame ionization detector to quantify the studied FAs. Using NMDS analysis (quantitative panel), great difference was found between krill from the BS compared to krill collected in the AS and the AP. The differences are reliable owing to the following 16 FAs, most of them trophic biomarkers of microphytoplankton, and suggest regional differences, mainly in abundance and ability of forage objects. CTD measurements discuss the abiotic factors (potential temperature, salinity, and chlorophyll “a”). Compensatory modifications of the composition of FA components in Antarctic krill inhabiting different water areas are a way of maintaining the species’ viability under certain and variable habitat conditions.

The abundance of the Russian sturgeon population in the Sea of Azov declined many times in the XX–XXI centuries. This paper presents long-term annual and spatial occurrence datasets to create statistical and machine learning models to better understand the distribution patterns as well as biological and ecological features. The annual dataset provides annually averaged results of environmental and biotic population estimates obtained by in situ observations for 1925–2024. The spatial occurrence dataset contains raw survey observations with a bottom trawl over the period of 2000–2024. Preliminary diagnostics of the annual dataset reveal no evidence of non-stationarity or significant outliers that cannot be explained by biological parameters. The published dataset allows any researcher to perform statistical and machine learning-based analysis in order to compare and describe the population abundance or spatial occurrence of Russian sturgeon in the Sea of Azov.

A new approach was applied for forecasting purposes of Russian sturgeon abundance in the Sea of Azov. Different types of supervised machine learning models were tested based on long-term data of Russian sturgeon abundance and ecosystem parameters of the Sea of Azov during the period 1970–2024. The best determination and the smallest mean average error were achieved by a random forest model. Among the neural networks, the Bayesian regularized neural network model showed good fitting, but it lost in accuracy against the random forest. Boosting-type models did not show a significant advantage in comparison with classic additive models. The reliability of the obtained solution using the random forest model surpassed the previously obtained results of the production model and was at the level of a cohort solution. The use of the random forest model made it possible to describe the period of depletion of the Russian sturgeon population in 2000–2024, in contrast to analytical models. Based on the results of the model type selection, a few sub-models were fitted for each ecosystem sector (or predictor selection technique): Full, Toppred, Hydro, and Food models to make predictions possible in the theoretical case of missing data. The best fitting and most accurate diagnostics were achieved by the Full (R² = 0.84, Adj.R² = 0.79) and Toppred (R² = 0.75, Adj.R² = 0.73) models. The Hydro sub-model also seems suitable (R² = 0.68, Adj.R² = 0.65) to predict Russian sturgeon abundance. Short-term forecasts were performed after the introduction of lags between ecosystem predictor factors and predictable sturgeon numbers (lag = 4 years). The Full, Toppred, and Food models predict a significant increase in the number of Russian sturgeons during 2025–2026 (up to ~5 M specimens), whereas the Hydro model predicts a decrease (sturgeon numbers in the range of 0.2–1.4 M specimens).