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Computational Approaches to Predict Indices of Cyanobacteria Toxicity.
Citation:
Kreakie, B., F. Nojavan, Jeff Hollister, AND Bryan Milstead. Computational Approaches to Predict Indices of Cyanobacteria Toxicity. New England Association of Environmental Biologists (NEAEB) Conference, Hartford, CT, March 14 - 16, 2017.
Impact/Purpose:
This work uses computational methods to predict both trophic state and microcystin occurrence. In addition to providing insight about the ecology of these systems, we are also able to compute our uncertainty of these predictions.
Description:
As nutrient inputs increase, productivity increases and lakes transition from low trophic state (e.g., oligotrophic) to higher trophic states (e.g., eutrophic). These broad trophic state classifications are good predictors of ecosystem health and the potential for ecosystem services (e.g., recreation, aesthetics, and fisheries). Additionally, some ecosystem disservices, such as cyanobacteria blooms, are also associated with increased nutrient inputs. Thus, trophic state can be used as a proxy for cyanobacteria bloom risk. To explore this idea, we construct two random forest models of trophic state (as determined by chlorophyll a concentration). First we define an “All Variable” model that estimates trophic state with both in situ and universally available data, and then we reduce this to a “GIS Only” model that uses only the universally available data. The “All Variables” model had a root mean square error (RMSE) of 0.09 and R2 of 0.8; whereas, the “GIS Only” model was 0.22 and 0.48 for RMSE and R2, respectively. Examining the “GIS Only” model (i.e., the model that has broadest applicability) we see that in spite of lower overall accuracy, it still has better than even odds (i.e., prediction probability is > 50%) of being correct in more than 1091 of the 1138 lakes included in this model. The “GIS Only” model has tremendous potential for exploring spatial trends at the national level since the datasets required to parameterize the model are ubiquitous. To further extend this analysis, we used the results of random forest modeling as a means of variable selection from which we developed a Bayesian multilevel model of microcystin (a common cyanobacteria toxin with human and animal health implications) concentrations. The Bayesian multilevel model allows us to ascertain the key drivers at the continental scale while still incorporating local influences at the ecoregion scale. This reduces uncertainty and improves risk assessment at the eco-regional or state scale. Our preliminary results show associations between microcystin and turbidity, total nutrients, and N:P ratios. These results will aid in the development of management strategies to improve lake water quality and to reduce cyanobacteria bloom risk and microcystin impacts.