The application of game theory-based machine learning modelling to assess climate variability effects on the sensitivity of lagoon ecosystem parameters

The extent of changes at the ecosystem level due to external dynamics from year to year of meteorological conditions is one of the basic determinants of climate change. The object of the study is the sensitivity of a set of environmental and planktonic factors shaping the ecosystem of a temperate shallow lagoon (the Vistula Lagoon, South Baltic) to weather fluctuations from year to year. The specific question concerned the short-term advantage of the sequence of environmental changes related to the impact of wind or those caused by the air temperature. The average speed in the prevailing westerly winds in the first summer research season was 3.05 m/s, while in the second it was 3.84 m/s. Simultaneously, the air temperature changed on average from 16.8 to 17.3 °C. The accuracy of the division of the two-year data set of ecosystemic parameters into the first colder and less windy and the second, warmer and windier summer was analyzed. For this purpose, several machine learning models were used. Next, the model with the highest accuracy was selected for the explanatory modelling based on game theory metrics Shapley Value. The analysis based on the interactions among ecological factors shows that the dynamics of the climate on a year-to-year scale can bring significantly more environmental changes in the shallow lagoon by an increase in wind speed rather than by an increase in air temperature. More windy weather in the summer in the subsequent year caused higher wind action, suspended solids, silicates and diatoms concentration. Simultaneously, the same conditions resulted in lower concentrations of dissolved organic carbon and nitrogen forms in water, accompanied by a reduction of Cyanobacteria biomass. The procedure presented in the study can be used for environmental prognostics of the environmental effects of climate change.