|Title||Predicting coastal algal blooms in Southern California|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||McGowan JA, Deyle ER, Ye H, Carter ML, Perretti CT, Seger KD, de Verneil A, Sugihara G|
|Keywords||empirical dynamic modeling; Harmful algal blooms; nonlinear forecasting; stochastic chaos|
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. This article is protected by copyright. All rights reserved.
Despite the challenge of understanding and predicting rare events arising from biological dynamics in a stochastic environment, we have shown how the equation-free framework of EDM can improve our understanding of coastal algal blooms and their prediction – an analysis made possible by the long-term monitoring at the SIO pier and subsequent long time series. The combination of better-resolved data to test these mechanistic hypotheses and more sophisticated analytical tools to make use of these data is the obvious path forward for future research. Thus, as the first century of studying of these phenomena in Southern California comes to a close (1) we have a general understanding that blooms arise as nonlinear perfect storms of environment and biology (stochastic chaos), (2) we have field evidence for the importance of water column stability and nutrients, (3) and finally, we have the ability to predict blooms (albeit imperfectly) as well as a path toward better prediction through new measurements and new methods of analysis.