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

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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.