Developing a nested, multiscale framework for modelling animal movement processes from long-term tracking data

了解动物运动背后的驱动因素是做出预测的关键，从而准确地为保护工作提供信息。然而，具有高认知水平的动物在多个时空尺度上做出运动决策；创造一个整体的“运动系统”，这只能用一个复杂的数学框架来表示。这个框架目前是缺乏的，限制了我们在延长的时间尺度上预测野生动物运动的能力，而多尺度过程可能会发生。该项目旨在开发一个框架，用于建模和统计估计驱动动物运动的认知过程在空间和时间尺度上的依赖关系。特别是，它将考虑在复杂的时空点过程模型中记录连续位置数据的观测过程中的不确定性。在生物学上适当的尺度上量化动物记忆、意志和交流等基本认知过程，无疑将有助于更好地预测真实运动过程的动态。

Understanding the drivers behind animal movement is key in making predictions which accurately inform conservation efforts. However, animals with high levels of cognition make movement decisions at multiple spatial and temporal scales; creating an overall 'movement system' which can only be represented with a complex mathematical framework. This framework is currently lacking, limiting our ability to predict the movement of animals in the wild over the extended timescales that multi-scale processes can be expected to occur. This project aims to develop the framework for modelling and statistically estimating the dependence between cognitive processes that drive animal movement across a hierarchy of spatial and temporal scales. In particular, it will account for uncertainty in the observation process of recording consecutive location data within a complex spatiotemporal point process model. Quantifying such fundamental cognitive processes as animal memory, volition, and communication, at biologically appropriate scales, will undoubtedly lead to better predictions for the dynamics of real movement processes.