Modelling and Inference for the Movement of Interacting Individuals

交互个体运动的建模和推理

• 批准号: 1949142
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1949142
• 关键词: Modelling; Inference; Movement; Interacting; Individuals

This project will look at continuous-time movement models for multiple tracked wild animals, e.g. using GPS, where tracking overlaps in time, allowing inference about interactions between individuals. The emphasis is likely to be on relatively small numbers of individuals. A key tool will be a recently developed inference technique for state-switching diffusion models - representing movement and behaviour - where the probabilities of switching between states depend on location. The method, known as "uniformization", is based on relating the process of interest to a well-understood simpler process which is uniform in space. The project will extend this methodology by looking at diffusion in a higher-dimensional space, to represent multiple individuals simultaneously, and at state-spaces with more complex structure, to represent more sophisticated behaviour. Algorithms to speed up the fitting of these models will also be developed. There are no formal external partners, but there is informal collaboration with ecologists at the Animal and Plant Health Agency, including access to GPS data on wild boar.

该项目将研究多种被追踪野生动物的连续时间运动模型，例如动物。使用 GPS，跟踪时间重叠，从而可以推断个体之间的交互。重点可能是相对较少的个人。一个关键工具将是最近开发的用于状态切换扩散模型的推理技术（代表运动和行为），其中状态之间切换的概率取决于位置。该方法称为“均匀化”，基于将感兴趣的过程与易于理解的空间均匀的简单过程相关联。该项目将通过研究高维空间中的扩散来扩展这种方法，以同时表示多个个体，并在具有更复杂结构的状态空间中表示更复杂的行为。还将开发加速这些模型拟合的算法。没有正式的外部合作伙伴，但与动植物卫生局的生态学家有非正式的合作，包括获取野猪的 GPS 数据。

项目成果

Modelling and inference for the movement of interacting animals

互动动物运动的建模和推理

• DOI: 10.1111/2041-210x.13468
• 发表时间: 2020
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Milner J

Modelling group movement with behaviour switching in continuous time.

通过连续时间内的行为切换对群体运动进行建模。

• DOI: 10.1111/biom.13412
• 发表时间: 2022
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Niu M