Developing novel hidden Markov models from a data science perspective to inform seabird conservation

从数据科学的角度开发新颖的隐马尔可夫模型，为海鸟保护提供信息

• 批准号: 2435550
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2435550
• 关键词: Developing; novel; hidden; Markov; models

Understanding animal movement and identifying the behaviours that influence animal movement is crucial to inform effective conservation management. Behaviours are often inferred from movement data and rarely validated because it is difficult to directly observe and record behaviours influencing animal movement in remote and hostile terrain such as the marine environment. Using data provided by the Joint Nature Conservation Committee (JNCC), the validity of behaviours inferred from movement data using hidden Markov models ( HMMs) is examined. Also, the temporal resolution of animal movement data can impact inference on animal behaviour. For instance, animal movement data obtained at a lower temporal resolution may not capture behaviours only obtainable at a finer temporal scale that comes at a cost sampling duration and GPS battery life. We investigate this problem from the optic of the Nyquist-Shannon sampling theorem (NSST) useful for selecting temporal resolutions in the animal movement trajectories such that there is no loss of information about the underlying latent behavioural process.

了解动物运动并识别影响动物运动的行为对于有效的保护管理至关重要。行为往往是从运动数据推断出来的，很少得到验证，因为很难直接观察和记录影响动物在海洋环境等偏远和恶劣地形中运动的行为。使用联合自然保护委员会（JNCC）提供的数据，使用隐马尔可夫模型（HALGORY）的运动数据推断的行为的有效性进行了检查。此外，动物运动数据的时间分辨率会影响对动物行为的推断。例如，以较低的时间分辨率获得的动物运动数据可能无法捕获仅在成本采样持续时间和GPS电池寿命的更精细的时间尺度下可获得的行为。我们调查这个问题从光学的Nyquist-Shannon采样定理（NSST）有用的选择时间分辨率的动物运动轨迹，这样就不会丢失有关潜在的行为过程的信息。