| Abstract |
Algal blooms have become a yearly occurrence in Lake Erie for some time now. These blooms are not only a nuisance but can also pose a risk to human health. Theoretically, early warning signals will exist prior to a shift in ecosystem state, i.e. an algal bloom. Indicators, such as increasing variance and rising autocorrelation close to 1, have been associated with transitions to alternate ecosystem states. These early warning signals have been observed in some whole-ecosystem experiments using quickest detection (QD) methods. The goal of this study was to determine if these early warning signals were detected in chlorophyll data prior to an algal bloom in a real-life ecosystem, Lake Erie. The QD method for detecting early warning signals associated with shifts in ecosystem states was used. In Lake Erie, the shift from a mixed phytoplankton state to a cyanobacterial dominated state was considered a transition to an alternate ecosystem state. Results showed that increasing variance before an algal bloom was not always detected, and therefore early warning signals of an impending algal bloom were not seen. The research suggests that examining phycocyanin, a pigment specific to blue-green algae, may provide more promising results in the future. If successful, this research could be used to provide warnings of impeding algal blooms to water treatment managers, allowing them to be prepared for the situation.
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