Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Abstract

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.

Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio

“…Lower streamflows occur during periods with high evapotranspiration and less turbulent waters, conditions conducive to cyanobacterial dominance. Bertani et al ( 2017 ) found a negative relation between bloom size and streamflow and suggested this was consistent with cyanobacterial growth being favored by lower summer flushing rates and higher residence time. In the current study, a seasonable variable was used in three models.…”

“…The influence of wind direction and lake-level change on microcystin concentrations is complex. Microcystis growth is favored during periods with high water stability; however, short wind-induced mixing events may have a positive effect on cyanobacterial growth by enhancing resuspension of nutrients (Bertani et al 2017 ).…”

“…Average conditions over larger time periods may be more important to the development of current microcystin concentrations than are conditions at the time of sampling. Bertani et al ( 2017 ) used three time-lagged variables (2, 8, and 30 days for wind velocity and stress, irradiance, temperature, and streamflow) in models to estimate cyanobacterial bloom size in the Western Lake Erie Basin. They stated that different time scales help to minimize collinearity among variables.…”

Assessing the spatial and temporal variability of the Detroit River and harmful algal blooms in western Lake Erie

“…Surface area calculations were then compared to existing areal reports from Stumpf et al (2012) and Bertani et al (2017). Sandusky Bay was masked from the analysis due to HAB persistence within the bay throughout the season and its lack of spatial interaction with the western basin.…”

“…Spring phosphorus loading from the Maumee River has a significant influence on the size and severity of HABs in western Lake Erie (Bertani et al, 2017;Obenour et al, 2014;Stumpf et al, 2012), but this does not influence the spatial and temporal variability of the Detroit River and was not included as it was not in the scope of this research. To identify and determine which environmental variables have the greatest impact on the Detroit River plume and bloom and how these characteristics seasonally vary, a variety of meteorological, chemical, and physical parameters were considered (Table 2.1).…”