Journal
ECOLOGICAL INDICATORS
Volume 130, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.ecolind.2021.108055
Keywords
Bayesian hierarchical model; harmful algal blooms; informative priors; predictive model; sequential updating
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Funding
- NOAA
- NOAA-GLERL [1986]
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This study developed a short-term forecasting model of particulate cyanobacterial toxin concentrations in Western Lake Erie using a Bayesian hierarchical modeling framework. The model parameters are allowed to vary annually and seasonally, making it suitable for short-term risk assessment. The modeling process demonstrates the use of informative priors in developing Bayesian models.
We developed a Bayesian hierarchical modeling framework to establish a short-term forecasting model of particulate cyanobacterial toxin concentrations in Western Lake Erie using chlorophyll a concentration as the predictor. The model evolves over time with additional data to reflect the changing dynamics of cyanobacterial toxin production. Specifically, parameters of the empirical relationship between the cyanobacterial toxin microcystin and chlorophyll a concentrations are allowed to vary annually and seasonally under a hierarchical framework. As such, the model updated using the most recent sampling data is suited to provide short-term forecasts. The reduced model predictive uncertainty makes the model a viable tool for risk assessment. Using data from the long-term Western Lake Erie harmful algal bloom monitoring program (2008-2018), we illustrate the model-building and model-updating process and the application of the model for short-term risk assessment. The modeling process demonstrates the use of the Bayesian hierarchical modeling framework for developing informative priors in Bayesian modeling.
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