Examining Algal Bloom Disturbances with High-Frequency Time Series and High-Resolution Spatial Data

Lakes are important ecosystems that are under threat from internal disturbances such as algal blooms and external forcings such as climate change. Algal blooms are widespread disturbances that can negatively affect ecosystems services. Understanding algal bloom dynamics can help prevent and mitigate the negative consequences including toxicity, disruption of potable water, and closure of recreational activities. As high frequency data become more accessible and cheaper, the data can be leveraged for improving the understanding of temporal and spatial patterns. This study used high frequency phytoplankton pigment and water quality data to explore disturbance and spatial dynamics. First, I applied a recently published disturbance-recovery algorithm to quantify the magnitude of algal blooms and time to recovery using high frequency pigment time series from several lakes. Results for the first study indicate that the algorithm can detect disturbance and recovery in experimental and monitored lakes. The algorithm performs best for experimental lakes where reference data is available, whereas the algorithm detects disturbances that are intense and occur at dissimilar times for monitored lakes. Second, I analyzed high resolution spatial-temporal data to identify spatial variability and hotspots in an experimental lake undergoing nutrient addition and in an adjacent, unperturbed reference lake. Results for the second study indicate that there is no long-lasting spatial structure and low spatial heterogeneity for both an experimental lake undergoing an algal bloom and a similarly sized reference lake. These studies show how advanced technology can reveal temporal and spatial dynamics of lakes, specifically those undergoing algal bloom disturbances.