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.
Ecology. 2017 May;98(5):1419-1433. doi: 10.1002/ecy.1804.
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.
南加州沿海浮游藻类水华的不规则出现已经引起人们关注一个多世纪了。虽然大型藻类水华可能对生态系统和人类健康产生重大负面影响，但科学界一直未能对这些事件进行预测，许多人开始认为它们是最终随机的现象。然而，生态动态的高度非线性性质可能会给人一种随机性的印象，并对传统方法（如模型拟合或方差分析）造成压力，以至于达到了崩溃的临界点。因此，从经典线性角度来看，这个问题难以解决，给人一种藻类水华根本不可预测的印象。在这里，我们使用无方程建模方法对加利福尼亚州拉霍亚沿海浮游植物动态的特殊时间序列研究，表明这些现象不是随机的，而是可以理解为受外部随机驱动（所谓的“随机混沌”）的非线性种群动态。这种建模方法与大量数据集的结合不仅使我们能够描述历史行为并澄清有关机制的现有假设，还可以对拉霍亚最近的藻类水华进行样本外预测，这些预测不在模型开发范围内。
