J.C. Ho and A.M. Michalak
Harmful algal blooms are becoming more frequent both in lakes globally and in Lake Erie specifically, where a bloom in 2014 led to a three-day drinking water ban for residents of Toledo, Ohio. In this review, we looked at past studies which tracked blooms in Lake Erie and found that there is a lack of consensus among studies. There is ambiguity over whether or not blooms occurred in specific years, how big they were, and/or when they occurred. Since ongoing research is focused on developing models for predicting future blooms, our findings indicate that this ambiguity could lead to poor predictive accuracy of these models. Moving forward, we advocate for more basic work on how different methods for tracking blooms agree or disagree in order to develop better predictive models.
Harmful algal blooms (HABs) are becoming increasingly common in freshwater ecosystems globally, raising complex questions about the factors that influence their initiation and growth. These questions have increasingly been answered through mechanistic and stochastic modeling efforts that rely on historical information about HABs in a given system for development, validation, and calibration. Therefore, understanding processes that control HABs is predicated on the ability to answer much more basic questions about what has actually occurred in a given system, namely questions of HAB occurrence, extent, intensity, and timing. Here we explore the state of the science in answering these basic questions; we use Lake Erie as a case study, where nearly two decades after the resurgence of HABs, a summer 2014 event caused a mandatory three day tap water ban for Toledo, Ohio. We find that, even for well-studied systems, unambiguous answers to basic questions about HAB occurrence are lacking, raising concerns about their use as a basis for addressing mechanistic questions about controlling factors. This ambiguity is found to be caused by differences in the methods used to track HABs, the specific harm being considered, the linkage to that harm (direct or indirect), the threshold defining harm, and spatiotemporal variability in sampling. Further work is therefore needed to integrate heterogeneous types of observations in order to better leverage existing and future monitoring programs, and to guide modeling efforts toward deeper understanding of HAB causes and consequences.