Using Long‐Term Ecological Datasets to Unravel the Impacts of Short‐Term Meteorological Disturbances on Phytoplankton Communities

Abstract

Extreme meteorological events such as storms are increasing in frequency and intensity, but our knowledge of their impacts on aquatic ecosystems and emergent system properties is limited. Understanding the ecological impacts of storms on the dynamics of primary producers remains a challenge that needs to be addressed to assess the vulnerability of freshwater ecosystems to extreme weather conditions and climate change. One promising approach to gain insights into storm impacts on phytoplankton community dynamics is to analyse long-term monitoring datasets. However, such an approach requires disentangling the impacts of short-term meteorological disturbances from the effects of the seasonal trajectories of meteorological conditions. To this end, we applied boosted regression tree models to phytoplankton time series from eight relatively large lakes on four continents, coupled with a procedure adapted to detect and quantify rare events. Overall, the patterns and potential drivers we identified provide important insights into the responses of lakes to short-term meteorological events and highlight differences in the response of phytoplankton communities according to lake morphological characteristics. Our results indicated that deepened thermoclines and lake-specific combinations of drivers describing altered thermal structures caused deviations from the typical trajectories of seasonal phytoplankton succession. For shallow polymictic lakes, shifts in phytoplankton succession also depended on changes in light availability. Overall, our study highlights the value of long-term monitoring to improve our understanding of phytoplankton sensitivity to short-term meteorological disturbances.