Detecting cyclicity in ecological time series
Detecting cyclicity in ecological time series
复制标题
检测生态时间序列的周期性
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Doebeli
中科院分区：
文献类型：
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作者：
Stilianos Louca;M. Doebeli
循环的种群动力学是生态学的核心兴趣。可靠地识别和量化种群的周期性对于理解调控机制及其在时空尺度上的变异性是有价值的。周期性可以使用时间序列的周期图分析来检测。周期图峰值的统计显著性通常针对不相关波动的零假设（也称为白色噪声）进行评估。在这里，我们表明，这种零假设是不足以在生态系统中的周期检测不可忽略的相关时间。作为一个替代零假设，我们提出了所谓的Ornstein-Uhlenbeck状态空间（OUSS）模型，它概括了白色噪声，允许时间相关性。我们证明其使用的机械原理，并证明其优点，使用简单的人口模型的数值模拟。我们表明，仅仅对比周期性对白色噪声大大增加了错误的周期检测率，可以导致...
Cyclic population dynamics are of central interest in ecology. Reliably identifying and quantifying the cyclicity of populations is valuable for the understanding of regulatory mechanisms and their variability across spatiotemporal scales. Cyclicity can be detected using periodogram analysis of time series. The statistical significance of periodogram peaks is commonly evaluated against the null hypothesis of uncorrelated fluctuations, also known as white noise. Here, we show that this null hypothesis is inadequate for cycle detection in ecosystems with non-negligible correlation times. As an alternative null hypothesis we propose the so-called Ornstein-Uhlenbeck state-space (OUSS) model, which generalizes white noise to allow for temporal correlations. We justify its use on mechanistic principles and demonstrate its advantages using numerical simulations of simple population models. We show that merely contrasting cyclicity against white noise greatly increases the false cycle detection rate and can lead ...