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Predicting coastal algal blooms in southern California.
McGowan, John A; Deyle, Ethan R; Ye, Hao; Carter, Melissa L; Perretti, Charles T; Seger, Kerri D; de Verneil, Alain; Sugihara, George.
Afiliación
  • McGowan JA; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
  • Deyle ER; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
  • Ye H; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
  • Carter ML; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
  • Perretti CT; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
  • Seger KD; National Marine Fisheries Service, Northeast Fisheries Science Center, Woods Hole, Massachusetts, 02543, USA.
  • de Verneil A; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, 92093, USA.
  • Sugihara G; School of Marine Science and Ocean Engineering, University of New Hampshire, Durham, New Hampshire, 03823, USA.
Ecology ; 98(5): 1419-1433, 2017 May.
Article en En | MEDLINE | ID: mdl-28295286
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Ecosistema / Eutrofización / Microalgas Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Ecology Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Ecosistema / Eutrofización / Microalgas Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Ecology Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos