Modeling Anthropogenic and Environmental Influences on Freshwater Harmful Algal Bloom Development Detected by MERIS Over the Central United States
Human and ecological health have been threatened by the increase of cyanobacteria harmful algal blooms (cyanoHABs) in freshwater systems. Successful mitigation of this risk requires understanding the factors driving cyanoHABs at a broad scale. To inform management priorities and decisions, we employed random forest modeling to identify major cyanoHAB drivers in 369 freshwater lakes distributed across 15 upper Midwest states during the 2011 bloom season (July – October). We used Cyanobacteria Index (CI)—a remotely sensed product derived from the MEdium Resolution Imaging Spectrometer (MERIS) aboard the European Space Agency’s Envisat satellite—as the response variable to obtain variable importance metrics for 88 landscape and lake physiographic predictor variables. Lakes were stratified into high and low elevation categories to further focus CI variable importance identification by anthropogenic and natural influences. ‘High elevation’ watershed land cover was primarily forest or natural vegetation, compared with ‘low elevation’ watersheds land cover dominated by anthropogenic landscapes (e.g. agriculture, municipalities, etc.). Results showed that eight of the top ten drivers across all 369 lakes were agrarian–related (i.e., nutrient application, tiled drainage, etc.). Five top variables for the low elevation lakes were positively correlated with CI (% artificially drained agricultural land, manure application rate, % crop, surface ammonium application rate, and % of agriculture untreated by sink). Of the top five variables in high elevation lakes, soil erodibility, soil organic matter, and percent wetland area were positively correlated to CI, while maximum 72-hour precipitation and water table depth were negatively correlated with CI. A classification and regression tree (CART) analysis on the top 25 variables for both high and low elevation lakes partitioned observations into progressively smaller groups to better explain variable interactions. This classification may allow water resource managers to mitigate inputs into specific freshwater systems to offset possible cyanobacteria development.