Understanding primary production dynamics in a large lake through model-based and empirical analyses
Abstract: Lakes are sentinels of weather, climate, and human-induced changes, which are manifested in physical and ecological response signals over both short and long time scales. Hence, they are natural laboratories for studying the ecosystem functioning, including the flow of matter and energy within the food webs and their feedback to the global carbon cycle, as well as for anticipating future changes. However, to gain a holistic understanding of lake processes, it is critical to combine high-frequency observations with mechanistic modelling. Here, we present empirical and model-based data analyses to study primary production dynamics in Lake Geneva. We demonstrate how deep mixing can trigger massive algal blooms on a seasonal time scale and how interactions between climate warming and reoligotrophication drive antagonistic responses in lake primary production. Furthermore, we predict that climate warming could bring back the symptoms of eutrophication. Additionally, we introduce a signal-processing method to estimate gross primary production (GPP) and ecosystem respiration from the observed diel oxygen fluctuations. The novelty of the method lies in filtering out the low-frequency signature of physical processes, such as internal waves. We found that more than 50% of GPP is respired back into the lake epilimnion and only 20% of organic carbon is exported to the deep.
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