eMB: Collaborative Research: Mechanistic models for seasonal avian migration: Analysis, numerical methods, and data analytics

eMB：协作研究：季节性鸟类迁徙的机制模型：分析、数值方法和数据分析

• 批准号: 2325195
• 负责人: King Yeung Lam
• 金额: $ 11.22万
• 依托单位: Ohio State 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=2325195&HistoricalAwards=false
• 关键词: eMB; Collaborative; Research; Mechanistic; models; eMB; Collaborative; Research; Mechanistic; models

Migratory animals including many bird species travel a spectacular distance annually between their summer breeding ground and overwintering site to track the appearance of key resources and maximize reproductive success. However, these migratory cycles are potentially disrupted as the timing of these resources are impacted by global climate change. To address the above knowledge gap, the interdisciplinary team of principle investigators (PIs) comprising a mathematician, biologist, and data scientists will develop novel mathematical models such as stochastic dynamic programming and agent-based models to gain mechanistic understanding of how migratory animals determine their routes and timing in response to environmental cues, and how those can be affected by climate change. The project will promote the use of the eBird database, which is a grassroots effort enabling citizens to directly contribute bird observations data using a mobile app. The project is a collaboration between Ohio State University and Oklahoma State University and offers valuable educational, training, and outreach opportunities. In addition to training of PhD students, the PIs propose to establish a K-12 program with a daylong workshop to engage the public and raise awareness of bird conservational efforts and impacts of climate change, as well as a summer undergraduate research program targeting underrepresented groups (ROMUS program at Ohio State). At the professional level, the PIs will organize synergistic activities to facilitate exchanges between mathematicians and biologists. These include a confirmed week-long workshop at Banff International Research Station in October 2024, for over 100 virtual and in-person participants. This research will improve our understanding of the effects of climate change on migrating bird populations and inform future conservation and management of wildlife. A critical question for understanding ecological responses to global change and conserving biodiversity in a changing world, is whether migrating animals can adjust their migration routes and schedules to track key resources even as the phenology of these resources shifts with climate change. In the case of birds with seasonal migratory routes crossing hemispheres, past studies have detected asynchrony between spring vegetation green-up and their arrival at breeding areas. This raised the concern that migrating birds may be negatively impacted by climate change. This interdisciplinary team of mathematician, biologist and data scientists will address the above knowledge gap by developing stochastic dynamic programming (SDP) models and agent-based models (ABM) for migrating animal populations. First, the aim is to develop an SDP model with switching costs in a continuous-time framework, in the context of optimal migration problem. The PIs will formulate and analyze the resulting Bellman equations and address the theoretical challenge of connecting individual migration decision to emergent population patterning. In addition, the PIs will also use spatially explicit agent-based modeling in parallel with and to extend the proposed mathematical modeling. Finally, the PIS will leverage remote sensing data on spring green-up across the Western Hemisphere with population level migration data for bird species from eBird database, for parameter estimation and model result comparison.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.

包括许多鸟类在内的迁徙动物每年在其夏季繁殖地和越冬地点旅行，以追踪关键资源的出现并最大程度地发挥生殖成功。但是，由于这些资源的时机受到全球气候变化的影响，这些迁徙周期可能会破坏。为了解决上述知识差距，包括数学家，生物学家和数据科学家组成的主要研究人员（PIS）的跨学科团队将开发出新的数学模型，例如随机动态编程和基于代理的基于代理的模型，以了解对迁徙动物的机械理解，以了解对环境线索的响应方式以及如何通过气候变化影响的方式，并确定这些模型。该项目将促进eBird数据库的使用，这是一项基层努力，使公民能够使用移动应用程序直接贡献鸟类观测数据。该项目是俄亥俄州立大学和俄克拉荷马州立大学之间的合作，并提供有价值的教育，培训和外展机会。 除了培训博士学位学生外，PIS还建议建立一项K-12计划，其中包括一整天的研讨会，以吸引公众并提高人们对鸟类保护的努力和气候变化影响的认识，以及针对夏季本科生的研究计划，针对俄亥俄州州立大学（ROMUS计划）。在专业层面，PI将组织协同活动，以促进数学家与生物学家之间的交流。其中包括2024年10月在Banff International Research Station举行的确认为期一周的研讨会，针对100多名虚拟和面对面的参与者。这项研究将提高我们对气候变化对迁移鸟类种群的影响的理解，并为未来的野生动植物保护和管理提供信息。理解对全球变化的生态反应和在不断变化的世界中保护生物多样性的一个关键问题是，迁移动物是否可以调整其迁移路线和时间表以跟踪关键资源，即使这些资源的物候学随气候变化而变化。对于鸟类穿越半球的季节性迁徙路线的鸟类，过去的研究发现了春季植被绿化与到达繁殖区域之间的异步。这引起了人们的担忧，即迁移的鸟类可能会受到气候变化的负面影响。这个数学家，生物学家和数据科学家的跨学科团队将通过开发随机动态编程（SDP）模型（SDP）模型和基于代理的模型（ABM）来解决上述知识差距。首先，目的是在最佳迁移问题的背景下，在连续时间框架中开发具有切换成本的SDP模型。 PI将制定和分析由此产生的Bellman方程，并应对将个体移民决定与新兴人群形态联系起来的理论挑战。此外，PIS还将与并行并扩展所提出的数学建模并平行使用基于空间的基于代理的建模。最后，PIS将利用西半球的春季绿色遥控数据，并从eBird数据库中使用鸟类物种的人口级别迁移数据，以进行参数估计和模型结果比较。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的。