A modeling analysis of spatial statistical indicators of thresholds for algal blooms

Predicting algal blooms both within and among aquatic ecosystems is important yet difficult because multiple factors promote and suppress blooms. Statistical indicators (e.g., variance and autocorrelation) based on time series can provide warning of transitions in diverse complex systems, including shifts from clear water to algal blooms. Analogous spatial indicators have been demonstrated with models and empirical data from vegetated terrestrial ecosystems. Here, we test the applicability of spatial indicators to algal blooms using a nutrient‐phytoplankton spatial model. We found that standard deviation and autocorrelation successfully distinguished bloom state and proximity to transitions, while skewness and kurtosis were more ambiguous. Our findings suggest certain spatial indicators are applicable to aquatic ecosystems despite dynamic physical–biological interactions that could reduce detectable signals. The growing capacity to collect spatial data on algal biomass presents an exciting opportunity for application and testing of spatial indicators to the study and management of blooms.