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

开发一个嵌套的多尺度框架，用于根据长期跟踪数据对动物运动过程进行建模

• 批准号: 2805547
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2805547
• 关键词: Developing; nested; multiscale; framework; modelling

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.

了解动物运动背后的驱动因素是做出准确为保护工作提供信息的预测的关键。然而，具有高认知水平的动物会在多个空间和时间尺度上做出运动决策。创建一个只能用复杂的数学框架来表示的整体“运动系统”。目前缺乏这个框架，限制了我们预测野生动物在长时间尺度内的运动的能力，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跨空间和时间尺度层次结构运动的认知过程之间的依赖性。特别是，它将解释在复杂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。