Spati-Temporal Dynamics of Bio-Production Systems by Chaos Time Series Analysis

决定论混沌的概念对生态学家很有吸引力，因为它蕴含着生态系统复杂性背后的潜在秩序。非线性时间序列分析(NTSA)用于研究确定性混沌。然而，大多数生态时间序列太短，不能进行NTSA，这需要数千个大小的时间序列。关于NTSA在生态动力学中的应用，我们需要开发合适的方法，在有限的数据和观测的情况下重建生态动力学。在这里，我们提出了从一个规模小于10的很短的生态时间序列中对动力学进行“集成重建”。在柑橘、坚果和橡子等大多数乔木作物中，产量在丰产年份和低产年份之间交替。Isagi等人的研究成果。提出了一种理论模型，该模型将桅杆描述为混沌，可以应用于交替轴承。我们使用一个包含48株树木7年产量的集成数据集来测试我们提出的方法，并在2002年、2003年和2004年三次通过一年的远期预测成功地验证了我们的方法。我们还证明了传统的方法，如Lyapunov谱和关联维，可以应用于系综重建方法。这一方法为实现对现实世界生态系统及其生物多样性的生态系统方法和/或适应性控制提供了必要的工具。我们还进行了遥感，以估计时空动态。

The concept of deterministic chaos is attractive for ecologists, because it implies an underlying order behind the complexity of ecological systems. Nonlinear time series analysis (NTSA) is used to investigate deterministic chaos. However, most ecological time series are too short to perform NTSA, which requires a time series whose size is in the thousands. On the application of NTSA on ecological dynamics., we need to develop appropriate way to reconstruct ecological dynamics with limited data, and observation. Here we propose "ensemble reconstruction" of the dynamics from a very short ecological time series whose size is smaller than ten. In most tree crops such as citrus, nuts and acorns, the yield alternates between high and low yielding years. Isagi, et al. proposed a theoretical model that describes masting as chaos that can be applied to alternate bearing. We used an ensemble dataset consisting of the yields of 48 individual trees over 7 years to test our proposed method and successfully validated this method by one-year forward prediction three times in 2002, 2003 and 2004. We also show that conventional tools such as Lyapunov spectrum and correlation dimension can be applied with the ensemble reconstruction method. This method offers an essential tool to realize an ecosystem approach towards and/or adaptive control of real world ecosystems and their biodiversity. We also conducted remote sensing to estimate the spatio-temporal dynamics.