From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data.
From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data.
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DOI：
10.1890/08-2159.1
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发表时间：
2010-05
期刊：
影响因子：
4.8
通讯作者：
Getz WM
中科院分区：
文献类型：
--
作者：
Polansky L;Wittemyer G;Cross PC;Tambling CJ;Getz WM
高分辨率的动物位置数据越来越多，需要分析方法和统计工具，可以容纳自然系统中固有的时间结构和瞬态动力学（非平稳性）。传统的分析往往假设不相关或弱相关的时间结构中的速度（净位移）时间序列构造使用连续的位置数据。我们建议，频率和时间-频率域的方法，体现了傅立叶变换和小波变换，可以作为有用的探针在动物运动数据的早期调查，刺激新的生态洞察力和问题。我们引入了一种新的运动模型与时变参数研究这些方法在动物运动的背景下。仿真研究表明，这些方法给出的频谱特征提供了一个有用的方法，统计检测和表征动物运动数据的时间依赖性。此外，我们的模拟提供了经验数据中观察到的光谱特征与预期动物活动的零假设之间的联系。我们的分析还表明，有没有一个特定的一对一的关系之间的光谱签名和行为类型，偏离预期的签名也是信息。按一天中的时间排列的净位移箱形图，并以常见的光谱特性为条件，可以帮助解释经验数据的光谱特征。第一个案例研究是基于一只狮子（Panthera leo）的运动轨迹，它显示了几个特征性的日常活动序列，包括与月光亮度相关的活动-休息周期。第二个例子的基础上，六对非洲布法罗（Syncerus caffer）说明了使用小波相干性，以表明他们的运动同步时，他们是在101公里的彼此，即使当个人的运动最好的描述为一个不相关的随机行走，提供了一个重要的空间基线的运动同步，并建议当地的行为线索在驱动运动模式中发挥了重要作用。最后，我们讨论了这些方法可能在指导适当灵活的概率模型连接运动与生物和非生物协变量的作用。
High-resolution animal location data are increasingly available, requiring analytical approaches and statistical tools that can accommodate the temporal structure and transient dynamics (non-stationarity) inherent in natural systems. Traditional analyses often assume uncorrelated or weakly correlated temporal structure in the velocity (net displacement) time series constructed using sequential location data. We propose that frequency and time–frequency domain methods, embodied by Fourier and wavelet transforms, can serve as useful probes in early investigations of animal movement data, stimulating new ecological insight and questions. We introduce a novel movement model with time-varying parameters to study these methods in an animal movement context. Simulation studies show that the spectral signature given by these methods provides a useful approach for statistically detecting and characterizing temporal dependency in animal movement data. In addition, our simulations provide a connection between the spectral signatures observed in empirical data with null hypotheses about expected animal activity. Our analyses also show that there is not a specific one-to-one relationship between the spectral signatures and behavior type and that departures from the anticipated signatures are also informative. Box plots of net displacement arranged by time of day and conditioned on common spectral properties can help interpret the spectral signatures of empirical data. The first case study is based on the movement trajectory of a lion (Panthera leo) that shows several characteristic daily activity sequences, including an active–rest cycle that is correlated with moonlight brightness. A second example based on six pairs of African buffalo (Syncerus caffer) illustrates the use of wavelet coherency to show that their movements synchronize when they are within ∼1 km of each other, even when individual movement was best described as an uncorrelated random walk, providing an important spatial baseline of movement synchrony and suggesting that local behavioral cues play a strong role in driving movement patterns. We conclude with a discussion about the role these methods may have in guiding appropriately flexible probabilistic models connecting movement with biotic and abiotic covariates.
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