Evaluating microcystin detection models for lakes in a nutrient-rich landscape applied under varying climate conditions

Algal blooms can threaten human health if toxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce toxins can help guide monitoring efforts, but variability in bloom severity and toxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 132 lakes in Iowa (USA) sampled between 2017-2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We also assessed if classification skill could be improved by assimilating the previous years’ monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer toxin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to decision-making for resource allocation, but similar investigations are needed in other regions to build further evidence for this approach. 

Primary Presenter: Jonathan Walter, University of California Davis (waltjo04@gmail.com)

Authors:

Grace Wilkinson, University of Wisconsin  (gwilkinson@wisc.edu)

Jonathan Walter, University of California, Davis  (jawalter@ucdavis.edu)

Ellen Albright, Univeristy of Wisconsin  (ellen.albright@wisc.edu)

Rachel King, Iowa State University  (rfleck@iastate.edu)

Eric Moody, Middlebury College  (ekmoody@middlebury.edu)

David Ortiz, University of Wisconsin  (dortiz4@wisc.edu)