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

混沌时间序列分析生物生产系统的时空动力学

基本信息

批准号：
14360148
负责人：
SAKAI Kenshi
金额：
$ 9.41万
依托单位：
Tokyo University of Agriculture and Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2005
项目状态：
已结题

项目摘要

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.

确定性混乱的概念对生态学家具有吸引力，因为它意味着生态系统复杂性背后的基本顺序。非线性时间序列分析（NTSA）用于研究确定性混乱。但是，大多数生态时间序列太短，无法执行NTSA，这需要一个时间序列的时间序列。关于NTSA在生态动力学上的应用。我们需要开发适当的方法，以有限的数据和观察来重建生态动力学。在这里，我们提出了一个非常短的生态时间序列的动力学的“集合重建”，其大小小于十。在大多数树木作物（例如柑橘，坚果和橡子）中，产量在高生产年之间交替。 Isagi等。提出了一个理论模型，将桅杆描述为可应用于替代轴承的混乱。我们使用了一个由7年内48个单独的树组成的合奏数据集来测试我们提出的方法，并在2002年，2003年和2004年通过一年的前期预测成功验证了该方法。我们还表明，可以使用Ensemble Rectustruction方法应用了Lyapunov Spectrum和相关维度等传统工具，例如Lyapunov Spectrum和Correlation dimension。该方法提供了一种必要的工具，可以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/或自适应控制的方法。我们还进行了遥感以估计时空动力学。