Predicting coastal algal blooms in southern California - John A. McGowan; Ethan R. Deyle; Hao Ye; Melissa L. Carter; Charles T. Perretti; Kerri D. Seger; Alain de Verneil; George Sugihara (Ecology, 2017)
The irregular appearance of planktonic algae blooms off the coast of southern California has been a source of wonder for over a century. Although large algal blooms can have significant negative impacts on ecosystems and human health, a predictive understanding of these events has eluded science, and many have come to regard them as ultimately random phenomena. However, the highly nonlinear nature of ecological dynamics can give the appearance of randomness and stress traditional methods—such as model fitting or analysis of variance—to the point of breaking. The intractability of this problem from a classical linear standpoint can thus give the impression that algal blooms are fundamentally unpredictable. Here, we use an exceptional time series study of coastal phytoplankton dynamics at La Jolla, CA , with an equation‐free modeling approach, to show that these phenomena are not random, but can be understood as nonlinear population dynamics forced by external stochastic drivers (so‐called “stochastic chaos”). The combination of this modeling approach with an extensive dataset allows us to not only describe historical behavior and clarify existing hypotheses about the mechanisms, but also make out‐of‐sample predictions of recent algal blooms at La Jolla that were not included in the model development.