Hsieh Chih-hao, Anderson Christian, Sugihara George
Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093, USA.
Am Nat. 2008 Jan;171(1):71-80. doi: 10.1086/524202.
Nonlinearity is important and ubiquitous in ecology. Though detectable in principle, nonlinear behavior is often difficult to characterize, analyze, and incorporate mechanistically into models of ecosystem function. One obvious reason is that quantitative nonlinear analysis tools are data intensive (require long time series), and time series in ecology are generally short. Here we demonstrate a useful method that circumvents data limitation and reduces sampling error by combining ecologically similar multispecies time series into one long time series. With this technique, individual ecological time series containing as few as 20 data points can be mined for such important information as (1) significantly improved forecast ability, (2) the presence and location of nonlinearity, and (3) the effective dimensionality (the number of relevant variables) of an ecological system.
非线性在生态学中既重要又普遍存在。虽然原则上可检测到，但非线性行为通常难以描述、分析，也难以从机制上纳入生态系统功能模型。一个明显的原因是，定量非线性分析工具需要大量数据（需要长时间序列），而生态学中的时间序列通常较短。在此，我们展示了一种有用的方法，该方法通过将生态上相似的多物种时间序列合并为一个长时间序列来规避数据限制并减少采样误差。利用这项技术，即使是包含少至20个数据点的单个生态时间序列，也能挖掘出诸如（1）显著提高的预测能力、（2）非线性的存在及位置，以及（3）生态系统的有效维度（相关变量的数量）等重要信息。
