Statistical methods for identifying unobserved structure in complex ecological and environmental data

识别复杂生态和环境数据中未观察到的结构的统计方法

• 批准号: RGPIN-2022-04750
• 负责人: LeosBarajas, Vianey
• 金额: $ 1.38万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765686
• 关键词: Statistical; methods; identifying; unobserved; structure

Due to the advancement of sensor technology, we can now remotely monitor ecological and environmental systems at fine temporal scales (e.g. multiple times a second to every few hours) with ease, over extended periods of time and space. This advancement comes at a crucial point as our environment undergoes massive disruptions due to climate change and anthropogenic influences. However, the technology and data that can be collected has outpaced the development of statistical methods designed to analyze it. A common goal in the analysis of sensor data is the identification of ecologically and environmentally relevant latent structures, for which the classes of Markov-switching, hidden Markov models, state-space models, and spatial models provide rich frameworks. In this proposal, I aim to (i) advance Bayesian Markov-switching models for ecological and environmental data, (ii) develop generative models for ecological processes over time and space, and (iii) develop statistical learning approaches for classification of video and movement data collected from animals. The HQP involved in the three objectives will form part of my research group, "Bayesian Ecological and Environmental Statistics (B.E.E.S.)", where the focus will be on statistical development of methodology that tackles pressing ecological and environmental problems and will form part of an encouraging and supportive community composed of myself and their peers. I am fully committed to building and empowering a cross- and interdisciplinary research group that advocates for the success of all, in particular by providing opportunities for those who are visible minorities, Indigenous or part of another historically underrepresented group.

由于传感器技术的进步，我们现在可以轻松地在更长的时间和空间内对生态和环境系统进行精细的时间尺度(例如每秒多次到每隔几个小时)的远程监测。这一进展发生在一个关键时刻，因为我们的环境正在经历由于气候变化和人为影响而造成的大规模破坏。然而，可以收集的技术和数据已经超过了为分析它而设计的统计方法的发展。传感器数据分析的一个共同目标是识别与生态和环境相关的潜在结构，马尔可夫切换类、隐马尔可夫模型、状态空间模型和空间模型为这些潜在结构提供了丰富的框架。在这项建议中，我的目标是(I)改进生态和环境数据的贝叶斯马尔可夫切换模型，(Ii)开发生态过程随时间和空间的生成模型，以及(Iii)开发统计学习方法，用于从动物收集的视频和运动数据的分类。参与这三个目标的HQP将成为我的研究小组“贝叶斯生态和环境统计(B.E.E.S.)”的一部分，该小组的重点将是统计方法的发展，以解决紧迫的生态和环境问题，并将成为由我和他们的同龄人组成的鼓励和支持社区的一部分。我完全致力于建立一个跨学科和跨学科的研究小组，倡导所有人的成功，特别是通过为那些明显属于少数群体、土著或其他历史上代表性不足的群体的人提供机会。