Modelling group movement with behaviour switching in continuous time.
Modelling group movement with behaviour switching in continuous time.
复制标题
通过连续时间内的行为切换对群体运动进行建模。
作者：
Niu M
本文提出了一种新的方法来模拟集体运动在连续时间与行为转换，同时跟踪野生或半驯化动物的动机。群体中的每个个体有时会被一个未被观察到的领头点所吸引。然而，每个个体的行为状态可以在“跟随”和“独立”之间切换。“跟随”运动通过线性随机微分方程建模，而“独立”运动建模为布朗运动。先导点的运动被建模为Ornstein - Uhlenbeck （OU）过程或brown运动（BM），这使得整个系统成为一个高维的Ornstein - Uhlenbeck过程，可能是一个内在的非平稳版本。提出了一种非齐次卡尔曼滤波马尔可夫链蒙特卡罗算法，用于估计每个个体在给定时间点的扩散和切换参数以及行为状态。该方法成功地恢复了模拟数据集的真实行为状态，并应用于模拟一组同时跟踪的驯鹿（Rangifer tarandus）。
This article presents a new method for modelling collective movement in continuous time with behavioural switching, motivated by simultaneous tracking of wild or semi‐domesticated animals. Each individual in the group is at times attracted to a unobserved leading point. However, the behavioural state of each individual can switch between ‘following’ and ‘independent’. The ‘following’ movement is modelled through a linear stochastic differential equation, while the ‘independent’ movement is modelled as Brownian motion. The movement of the leading point is modelled either as an Ornstein‐Uhlenbeck (OU) process or as Brownian motion (BM), which makes the whole system a higher‐dimensional Ornstein‐Uhlenbeck process, possibly an intrinsic non‐stationary version. An inhomogeneous Kalman filter Markov chain Monte Carlo algorithm is developed to estimate the diffusion and switching parameters and the behaviour states of each individual at a given time point. The method successfully recovers the true behavioural states in simulated data sets , and is also applied to model a group of simultaneously tracked reindeer (Rangifer tarandus).