Nams Vilis O
Department of Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS, Canada.
Acta Biotheor. 2013 Jun;61(2):269-84. doi: 10.1007/s10441-013-9182-8. Epub 2013 Mar 5.
Animal movement models allow ecologists to study processes that operate over a wide range of scales. In order to study them, continuous movements of animals are translated into discrete data points, and then modelled as discrete models. This discretization can bias the representation of the movement path. This paper shows that discretizing correlated random movement paths creates a biased path by creating correlations between successive turning angles. The discretization also biases statistical tests for correlated random walks (CRW) and causes an overestimate in distances travelled; a correction is given for these biases. This effect suggests that there is a natural scale to CRWs, but that distance-discretized CRWs are in a sense, scale invariant. Perhaps a new null model for continuous movement paths is needed. Authors need to be aware of the biases caused by discretizing correlated random walks, and deal with them appropriately.
动物运动模型使生态学家能够研究在广泛尺度上运行的过程。为了研究这些过程，动物的连续运动被转化为离散的数据点，然后作为离散模型进行建模。这种离散化可能会使运动路径的表示产生偏差。本文表明，对相关随机运动路径进行离散化会通过在连续转弯角度之间建立相关性而产生有偏差的路径。离散化还会使相关随机游走（CRW）的统计检验产生偏差，并导致对行进距离的高估；针对这些偏差给出了一种校正方法。这种效应表明CRW存在一个自然尺度，但从某种意义上说，距离离散化的CRW是尺度不变的。也许需要一个用于连续运动路径的新零模型。作者需要意识到离散化相关随机游走所造成的偏差，并适当地加以处理。
