Roberts Stephen, Guilford Tim, Rezek Iead, Biro Dora
Machine Learning Research Group, University of Oxford, UK.
J Theor Biol. 2004 Mar 7;227(1):39-50. doi: 10.1016/j.jtbi.2003.07.002.
In these two companion papers, we introduce a new approach to the analysis of bird navigation which brings together several novel mathematical and technical applications. Miniaturized GPS logging devices provide track data of sufficiently high spatial and temporal resolution that considerable variation in flight behaviour can be observed remotely from the form of the track alone. We analyse a fundamental measure of bird flight track complexity, spatio-temporal entropy, and explore its state-like structure using a probabilistic hidden Markov model. The emergence of a robust three-state structure proves that the technique has analytical power, since this structure was not obvious in the tracks alone. We propose the hypothesis that positional entropy is indicative of underlying navigational uncertainty, and that familiar area navigation may break down into three states of navigational confidence. By interpreting the relationship between these putative states and features on the map, we are able to propose a number of hypothetical navigational strategies feeding into these states. The first of these two papers details the novel technical developments associated with this work and the second paper contains a navigational interpretation of the results particularly with respect to visual features of the landscape.
在这两篇配套论文中，我们介绍了一种分析鸟类导航的新方法，该方法融合了多种新颖的数学和技术应用。小型化GPS记录设备提供了具有足够高时空分辨率的轨迹数据，仅从轨迹形式就能远程观察到飞行行为的显著差异。我们分析了鸟类飞行轨迹复杂性的一项基本指标——时空熵，并使用概率隐马尔可夫模型探索其类似状态的结构。一个稳健的三状态结构的出现证明了该技术具有分析能力，因为这种结构在单独的轨迹中并不明显。我们提出假设，位置熵指示潜在的导航不确定性，并且熟悉区域导航可能会分解为三种导航置信状态。通过解释这些假定状态与地图上特征之间的关系，我们能够提出一些进入这些状态的假设导航策略。这两篇论文中的第一篇详细介绍了与这项工作相关的新颖技术发展，第二篇论文则包含了对结果的导航解释，特别是关于景观视觉特征方面的解释。
