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A Bayesian Multilevel Model for Microcystin Prediction in Lakes of the Continental United States,
Citation:
Nojavan, F., B. Kreakie, AND Jeff Hollister. A Bayesian Multilevel Model for Microcystin Prediction in Lakes of the Continental United States,. Ecological Society of America (ESA) Conference, Portland, OR, August 06 - 11, 2017.
Impact/Purpose:
This work provides a probabilistic framework for predicting microcystin presences in lakes. It would allow for insights to be made about how changes in nutrient concentrations could potentially change toxin levels.
Description:
The frequency of cyanobacteria blooms in North American lakes is increasing. A major concernwith rising cyanobacteria blooms is microcystin, a common cyanobacterial hepatotoxin. Toexplore the conditions that promote high microcystin concentrations, we analyzed the US EPANational Lake Assessment (NLA) dataset collected in the summer of 2007. The NLA datasetis reported for nine eco-regions. We used the results of random forest modeling as a means ofvariable selection from which we developed a Bayesian multilevel model of microcystin concentrations.Model parameters under a multilevel modeling framework are eco-region specific, butthey are also assumed to be exchangeable across eco-regions for broad continental scaling. Theexchangeability assumption ensures that both the common patterns and eco-region specific featureswill be reflected in the model. Furthermore, the method incorporates appropriate estimatesof uncertainty. Our preliminary results show associations between microcystin and turbidity, totalnutrients, and N:P ratios. The NLA 2012 will be used for Bayesian updating. The results willhelp develop management strategies to alleviate microcystin impacts and improve lake quality.