The effect of irradiance, vertical mixing and temperature on spring phytoplankton dynamics under climate change:...

Spring algal development in deep temperate lakes is thought to be strongly influenced by surface irradiance, vertical mixing and temperature, all of which are expected to be altered by climate change. Based on long-term data from Lake Constance, we investigated the individual and combined effects of these variables on algal dynamics using descriptive statistics, multiple regression models and a process-oriented dynamic simulation model. The latter considered edible and less-edible algae and was forced by observed or anticipated irradiance, temperature and vertical mixing intensity. Unexpectedly, irradiance often dominated algal net growth rather than vertical mixing for the following reason: algal dynamics depended on algal net losses from the euphotic layer to larger depth due to vertical mixing. These losses strongly depended on the vertical algal gradient which, in turn, was determined by the mixing intensity during the previous days, thereby introducing a memory effect. This observation implied that during intense mixing that had already reduced the vertical algal gradient, net losses due to mixing were small. Consequently, even in deep Lake Constance, the reduction in primary production due to low light was often more influential than the net losses due to mixing. In the regression model, the dynamics of small, fast-growing algae was best explained by vertical mixing intensity and global irradiance, whereas those of larger algae were best explained by their biomass 1 week earlier. The simulation model additionally revealed that even in late winter grazing may represent an important loss factor during calm periods when losses due to mixing are small. The importance of losses by mixing and grazing changed rapidly as it depended on the variable mixing intensity. Higher temperature, lower global irradiance and enhanced mixing generated lower algal biomass and primary production in the dynamic simulation model. This suggests that potential consequences of climate change may partly counteract each other.