Kareiva P M, Shigesada N
Division of Biology, Brown University, 02912, Providence, RI, USA.
Department of Biophysics, Kyoto University, 606, Kyoto, Japan.
Oecologia. 1983 Feb;56(2-3):234-238. doi: 10.1007/BF00379695.
This paper develops a procedure for quantifying movement sequences in terms of move length and turning angle probability distributions. By assuming that movement is a correlated random walk, we derive a formula that relates expected square displacements to the number of consecutive moves. We show this displacement formula can be used to highlight the consequences of different searching behaviors (i.e. different probability distributions of turning angles or move lengths). Observations of Pieris rapae (cabbage white butterfly) flight and Battus philenor (pipe-vine swallowtail) crawling are analyzed as a correlated random walk. The formula that we derive aptly predicts that net displacements of ovipositing cabbage white butterflies. In other circumstances, however, net displacements are not well-described by our correlated random walk formula; in these examples movement must represent a more complicated process than a simple correlated random walk. We suggest that progress might be made by analyzing these more complicated cases in terms of higher order markov processes.
本文开发了一种根据移动长度和转弯角度概率分布来量化运动序列的程序。通过假设运动是相关随机游走,我们推导出一个将预期平方位移与连续移动次数相关联的公式。我们表明,这个位移公式可用于突出不同搜索行为(即转弯角度或移动长度的不同概率分布)的后果。将粉蝶(菜粉蝶)飞行和北美 Pipe - vine 燕尾蝶爬行的观察结果作为相关随机游走进行分析。我们推导的公式恰当地预测了产卵菜粉蝶的净位移。然而,在其他情况下,我们的相关随机游走公式并不能很好地描述净位移;在这些例子中,运动必定代表了一个比简单相关随机游走更复杂的过程。我们建议通过根据高阶马尔可夫过程分析这些更复杂的情况来取得进展。