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).