Detecting chaos in a citrus orchard: Reconstruction of nonlinear dynamics from very short ecological time series
Detecting chaos in a citrus orchard: Reconstruction of nonlinear dynamics from very short ecological time series
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DOI：
10.1016/j.chaos.2007.01.144
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发表时间：
2008-12
影响因子：
7.8
通讯作者：
K. Sakai;Y. Noguchi;S. Asada
中科院分区：
文献类型：
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作者：
K. Sakai;Y. Noguchi;S. Asada
自May的经典工作以来，从短时间生态时间序列重构非线性动力学一直是近二十年来生态学领域的一个热门课题。非线性时间序列分析（NTSA）被用来研究确定性混沌。然而，大多数生态时间序列太短，执行NTSA，这通常需要一个时间序列的大小是在数千。在这里，我们提出了一种方法来重建本地动态从一个非常短的生态时间序列的数据点小于10。对于大多数树木作物，如柑橘、坚果和橡子，产量在高产和低产年份之间交替。Isagi等人[Isagi Y，Sugimura K，Sumida A，Ito H.桅杆是如何发生和同步的？J Theor Biol 1997;187：231-9]提出了一种将桅杆结构描述为混沌的机械模型，并可应用于交替轴承。在这里，我们使用了一个集合数据集，包括48个单独的树木的产量超过7年来测试我们提出的方法，并成功地验证了这种方法的一年前预测三次在2002年，2003年和2004年。我们还显示了NTSA工具的适用性，如李雅普诺夫指数，关联维数和确定性非线性预测重建的局部动力学。
The reconstruction of nonlinear dynamics from short ecological time series has been an attractive subject in ecology for the last two decade since May’s classical work. Nonlinear time series analysis (NTSA) is used to investigate deterministic chaos. However, most ecological time series are too short to perform NTSA, which usually requires a time series whose size is in the thousands. Here we propose a way to reconstruct local dynamics from a very short ecological time series whose data point is smaller than ten. For most tree crops such as citrus, nuts and acorns, the yield alternates between high- and low-bearing years. Isagi et al. [Isagi Y, Sugimura K, Sumida A, Ito H. How does masting happen and synchronize? J Theor Biol 1997;187:231–9] proposed a mechanistic model that describes masting as chaos and can be applied to alternate bearing. Here we have used an ensemble data set consisting of the yields of 48 individual trees over seven years to test our proposed method and have successfully validated this method by a one-year forward prediction three times in 2002, 2003 and 2004. We also show the applicability of NTSA tools such as Lyapunov exponents, correlation dimension and deterministic nonlinear prediction on the reconstructed local dynamics.