eMB: Collaborative Research: New mathematical approaches for understanding spatial synchrony in ecology

eMB：协作研究：理解生态学空间同步的新数学方法

• 批准号: 2325078
• 负责人: Karen Abbott
• 金额: $ 42.49万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325078&HistoricalAwards=false
• 关键词: eMB; Collaborative; Research; New; mathematical

Understanding what drives ecological dynamics is an important challenge, with difficulties arising both in measuring ecological populations and identifying the relevant dynamical interactions. Given this, a useful approach is to base ideas on measurements that have the most information, even when the accuracy is not great, which suggests using dynamics that vary both in space and time. This proposal builds on this premise to develop models from statistical physics combined with data obtained from remote sensing. The underlying correspondence between ecological dynamics and statistical physics models is accomplished by coarse graining the ecological data and using models that permit only a small number of states of the population. This approach complements more traditional mathematical approaches based on dynamical systems and is well suited to crude data. The overall goal will be to predict the features that either facilitate or prevent synchrony in dynamics across space through time. This will yield new understanding of ecological dynamics with potential for improving conservation and agricultural practices.The overall goal of this project is to develop novel mathematical approaches for spatio-temporal dynamics in ecological systems, with a focus on relevant time scales. Understanding the processes that have led to spatial synchrony in ecological populations across space and at multiple temporal scales is a substantial challenge, made more urgent by the need to understand and predict the impacts of a changing climate. Most of the longstanding mathematical tools for ecological dynamics focus on asymptotic behavior, but real ecological systems are likely strongly influenced by transient behavior. In addition, ecological data are often very noisy, generating substantial uncertainty to which our methods much be robust. The Investigators will apply novel and highly complementary quantitative methods to questions about the origins and consequences of ecological synchrony. First, the Investigators will use the idea of Ising universality – well established in statistical physics but severely underdeveloped for its potential biological applications – to consider synchronization in a detail-independent manner. The Investigators will then apply modern machine learning techniques to better understand the details of how actual synchrony patterns arise, using remotely sensed orchard data as a case study. Mechanistic models of intermediate complexity will serve as a bridge. By connecting the simplified but universal Ising model description with the data-intensive machine learning methods the Investigators seek to validate, improve and better understand both approaches to understanding ecological synchrony. Synchrony and spatial patterning are central to conservation biology and public health, and uncovering universal rules for pattern formation will open a path to new insights in these fields.This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS) and the Division of Environmental Biology (DEB) in the Directorate for Biological Sciences (BIO), Population and Community Ecology Cluster (PEC).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解驱动生态动态的因素是一个重要的挑战，在测量生态种群和识别相关的动态相互作用方面都存在困难，因此，一种有用的方法是将想法建立在拥有最多信息的测量基础上，即使准确性不是很高。 ，它建议使用在空间和时间上变化的动力学来开发统计物理学模型，并结合从遥感中获得的数据。生态动力学和统计物理模型之间的潜在对应关系是通过对生态进行粗粒度化来实现的。数据并使用仅允许少数状态的模型这种方法补充了基于动力系统的传统数学方法，并且非常适合原始数据，其总体目标是预测促进或阻止跨时间动态同步的特征。具有改善保护和农业实践潜力的生态动力学。该项目的总体目标是开发生态系统时空动力学的新颖数学方法，重点是了解导致空间同步的过程。跨空间和多个时间的生态种群规模是一个巨大的挑战，由于需要了解和预测剧烈变化的气候的影响，大多数长期存在的生态动力学数学工具都关注渐近行为，但真实的生态系统可能受到瞬态行为的影响。此外，生态数据通常非常嘈杂，产生很大的不确定性，而我们的方法对此非常稳健。研究人员将应用新颖且高度互补的定量方法来解决有关生态同步的起源和后果的问题。伊辛普遍性——好吧在统计物理学中建立，但其潜在的生物学应用严重不足——以独立于细节的方式考虑同步，然后研究人员将使用遥感果园数据，应用现代机器学习技术来更好地理解实际同步模式如何出现的细节。一个案例研究。通过将简化但通用的伊辛模型描述与数据密集型机器学习方法连接起来，研究人员寻求验证、改进和更好地理解这两种理解生态同步的方法。同步性和空间模式是保护生物学和公共卫生的核心，揭示模式形成的普遍规则将为这些领域的新见解开辟道路。该项目由数学理事会数学科学部 (DMS) 联合资助和物理科学 (MPS) 以及生物科学理事会 (BIO) 下的环境生物学部 (DEB)、人口和社区生态集群 (PEC)。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。