Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, NJ 08901-8551, USA.
Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2201-11. doi: 10.1098/rstb.2010.0078.
Modern animal movement modelling derives from two traditions. Lagrangian models, based on random walk behaviour, are useful for multi-step trajectories of single animals. Continuous Eulerian models describe expected behaviour, averaged over stochastic realizations, and are usefully applied to ensembles of individuals. We illustrate three modern research arenas. (i) Models of home-range formation describe the process of an animal 'settling down', accomplished by including one or more focal points that attract the animal's movements. (ii) Memory-based models are used to predict how accumulated experience translates into biased movement choices, employing reinforced random walk behaviour, with previous visitation increasing or decreasing the probability of repetition. (iii) Lévy movement involves a step-length distribution that is over-dispersed, relative to standard probability distributions, and adaptive in exploring new environments or searching for rare targets. Each of these modelling arenas implies more detail in the movement pattern than general models of movement can accommodate, but realistic empiric evaluation of their predictions requires dense locational data, both in time and space, only available with modern GPS telemetry.
现代动物运动建模源于两个传统。基于随机游走行为的拉格朗日模型适用于单个动物的多步轨迹。连续的欧拉模型描述了随机实现的平均预期行为，并且在个体集合中很有用。我们展示了三个现代研究领域。（i） 栖息地形成模型描述了动物“定居”的过程，通过包含一个或多个吸引动物运动的焦点来完成。（ii）基于记忆的模型用于预测累积经验如何转化为偏向的运动选择，采用强化随机游走行为，以前的访问增加或减少重复的概率。（iii） Lévy 运动涉及步长分布，与标准概率分布相比，步长分布过度分散，并且在探索新环境或搜索稀有目标时具有适应性。这些建模领域中的每一个都意味着运动模式比运动的一般模型更详细，但要对其预测进行现实的经验评估，需要在时间和空间上都具有密集的位置数据，而现代 GPS 遥测技术仅提供这种数据。
