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Cyanobacteria are a primary taxonomic group associated with harmful algal blooms in lakes. As such understanding the drivers of cyanobacteria presence has important implications for lake management and for the protection of human and ecosystem health. Chlorophyll a concentration, a measure of the biological productivity of a lake, is one such driver and is largely, although not exclusively, determined by nutrient inputs. 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/disservices (e.g. recreation, aesthetics, fisheries, and harmful algal blooms). Thus, our existing models of trophic state might used to predict cyanobacteria. We test this idea and compare the performance of two models of trophic state (as determined by chlorophyll a concentration). Model 1 estimates trophic state with nutrients alone and model 2 uses nutrients plus landscape composition. We use random forest modeling to determine the out of bag (OOB) error for both models to compare the error associated with each variable. Lastly, we use the best model to compare presence/absence of several cyanobacteria taxa relative to trophic state.

Expanding models of lake trophic state to predict cyanobacteria in lakes.