title: Predicting coastal algal blooms in southern California.
abstract: 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.
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
authors.forename: John A
authors.initials: JA
authors.lastname: McGowan
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
authors.forename: Ethan R
authors.initials: ER
authors.lastname: Deyle
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
authors.forename: Hao
authors.identifier: http://orcid.org/0000-0002-8630-1458
authors.initials: H
authors.lastname: Ye
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
authors.forename: Melissa L
authors.initials: ML
authors.lastname: Carter
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.|National Marine Fisheries Service, Northeast Fisheries Science Center, Woods Hole, Massachusetts, 02543, USA.
authors.forename: Charles T
authors.initials: CT
authors.lastname: Perretti
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.|School of Marine Science and Ocean Engineering, University of New Hampshire, Durham, New Hampshire, 03823, USA.
authors.forename: Kerri D
authors.initials: KD
authors.lastname: Seger
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.|Institut Méditerranéen d'Océanologie, Campus de Luminy Case 901 13288, Marseille, France.
authors.forename: Alain
authors.initials: A
authors.lastname: de Verneil
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
authors.forename: George
authors.initials: G
authors.lastname: Sugihara
mesh_terms: D002140:California; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005068:Eutrophication; D006801:Humans; D058086:Microalgae / Q000254:growth & development*; D010839:Phytoplankton / Q000254:growth & development; D010933:Plankton / Q000254:growth & development
keywords: empirical dynamic modeling; harmful algal blooms; nonlinear forecasting; stochastic chaos