Effects of multiple stressors on cyanobacteria abundance vary with lake type.

Blooms of cyanobacteria are a current threat to global water security that is expected to increase in the future because of increasing nutrient enrichment, increasing temperature and extreme precipitation in combination with prolonged drought. However, the responses to multiple stressors, such as those above, are often complex and there is contradictory evidence as to how they may interact. Here we used broad scale data from 494 lakes in central and northern Europe, to assess how cyanobacteria respond to nutrients (phosphorus), temperature and water retention time in different types of lakes. Eight lake types were examined based on factorial combinations of major factors that determine phytoplankton composition and sensitivity to nutrients: alkalinity (low and medium-high), colour (clear and humic) and mixing intensity (polymictic and stratified). In line with expectations, cyanobacteria increased with temperature and retention time in five of the eight lake types. Temperature effects were greatest in lake types situated at higher latitudes, suggesting that lakes currently not at risk could be affected by warming in the future. However, the sensitivity of cyanobacteria to temperature, retention time and phosphorus varied among lake types highlighting the complex responses of lakes to multiple stressors. For example, in polymictic, medium-high alkalinity, humic lakes cyanobacteria biovolume was positively explained by retention time and a synergy between TP and temperature, while in polymictic, medium-high alkalinity, clear lakes only retention time was identified as an explanatory variable. These results show that, although climate change will need to be accounted for when managing the risk of cyanobacteria in lakes, a "one-size fits-all" approach is not appropriate. When forecasting the response of cyanobacteria to future environmental change, including changes caused by climate and local management, it will be important to take this differential sensitivity of lakes into account.

Modelling of ecological status of Polish lakes using deep learning techniques.

Since 2000, after the Water Framework Directive came into force, aquatic ecosystems' bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered comprehensive hydrobiological databases. The amount of available data increases with each subsequent year of monitoring, and the efficient analysis of these data requires the use of proper mathematical tools. Our study challenges the comparison of the modelling potential between four indices for the ecological status assessment of lakes based on three groups of aquatic organisms, i.e. phytoplankton, phytobenthos and macrophytes. One of the deep learning techniques, artificial neural networks, has been used to predict values of four biological indices based on the limited set of the physicochemical parameters of water. All analyses were conducted separately for lakes with various stratification regimes as they function differently. The best modelling quality in terms of high values of coefficients of determination and low values of the normalised root mean square error was obtained for chlorophyll a followed by phytoplankton multimetric. A lower degree of fit was obtained in the networks for macrophyte index, and the poorest model quality was obtained for phytobenthos index. For all indices, modelling quality for non-stratified lakes was higher than this for stratified lakes, giving a higher percentage of variance explained by the networks and lower values of errors. Sensitivity analysis showed that among physicochemical parameters, water transparency (Secchi disk reading) exhibits the strongest relationship with the ecological status of lakes derived by phytoplankton and macrophytes. At the same time, all input variables indicated a negligible impact on phytobenthos index. In this way, different explanations of the relationship between biological and trophic variables were revealed.