Ultra-high frequency water quality sonde data from Lake George and Chautauqua Lake, NY, 2021

Forecasting rapid ecological change represents a major challenge in environmental science. In aquatic ecosystems, the shift from clearwater conditions to an algal bloom-dominated state represents an important transition resulting in harmful algal blooms (HABs). Methods for forecasting HABs using sensors are problematic, as lower frequency sampling can miss early warning indicators. Here, using sensor data from two lakes we show that antecedent information essential to forecasting HABs was best characterized using ultra-high frequencies (UHF; sampling ≤ 1 second). This publication is comprised of four individual datasets, each containing water quality measurements sampled at 4 Hz using an EXO2 sonde from two lakes in New York State, USA (Lake George and Chautauqua Lake). An accompanying R script is provided to read in each dataset, format it, and undertake a variety of analyses to demonstrate the utility of UHF data in identifying subtle environmental changes linked to HABs. The first dataset, "CHQ_StaticEXO.csv," includes UHF data from a stationary EXO2 sonde deployed in the South Basin of Chautauqua Lake, NY. This dataset spans a 24-hour period and is utilized to examine the behavior of different water quality sensor technologies during the rapid onset of a HAB. The second and third datasets, "LG_HarrisBayVP.csv" and "LG_HarrisBayVP_Cal.csv" were collected using a vertical profiler in Lake George, NY. These datasets provide UHF phycocyanin fluorescence data from a profiling EXO2 sonde over a two-month period, and are used to demonstrate how antecedent information critical for forecasting HABs is best characterized using UHF frequencies. Lastly, the dataset "GloeotrichiaExp.csv" was gathered as part of a laboratory experiment measuring cultured Gloeotrichia sp. colonies. As with the Lake George datasets, this dataset includes phycocyanin fluorescence data from an EXO2 sonde collected at 4 Hz, and is used to help explain some of the trends observed in the previous three datasets. Together, these four datasets were analyzed in the manuscript titled "Ultra-high frequency sensing enables forecasting of rapid environmental change associated with the onset of harmful algal blooms."