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NOBEL PRIZE 2026

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NSFDirectorate for Mathematical and Physical Sciences

Collaborative Research: Quantifying Fragmentation Impacts on Ecological Dynamics: A Trait-Based Framework

Active
Institution
OHIO STATE UNIVERSITY, THE, OH
Principal investigator
King Yeung Lam
Funding
$130K
Duration
2026–2029
Open portfolio

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Abstract

Habitat fragmentation is a major driver of global biodiversity loss, yet predicting how species respond to fragmented landscapes remains a fundamental challenge. This project develops and empirically validates new mathematical models that incorporate species-specific traits, recognizing that differences in dispersal and reproductive strategies lead to distinct responses to habitat fragmentation. By integrating mathematical modeling with laboratory experiments and field observations, the research will generate practical guidance for designing wildlife corridors, restoring degraded ecosystems, and improving habitat connectivity. The resulting computational tools and open-source software will support conservation planning, while course-based undergraduate research, workshops, and outreach activities will broaden participation in quantitative biology and help translate mathematical discoveries into conservation practice. Despite being among the top five direct drivers of global biodiversity loss, habitat fragmentation remains the subject of a contentious debate, largely due to the ambiguous definition and measurement. This project aims to predict and explain species responses to fragmentation by integrating mathematical modeling with controlled laboratory experiments and long-term field data. At the micro level, the project develops a novel partial differential equation (PDE) framework that incorporates edge effects and emphasizes biological trade-offs within local patchy landscapes and along patch boundaries. Scaling up to larger spatial scales (macro level), the project employs a metapopulation ordinary differential equation (ODE) model to examine the impact of patch configurations by introducing a novel fitness variation index, leveraging connectivity parameterizations derived from the PDE model and empirical data. Model parameters and predictions will be calibrated and validated using controlled Caenorhabditis elegans laboratory experiments and long-term field datasets of freshwater macroinvertebrate communities. Ultimately, these mathematical frameworks will be applied to conservation planning by evaluating habitat restoration, corridor design, reserve configuration, and species reintroduction strategies under alternative fragmentation scenarios. The proposed research and dissemination activities will bridge theory and practice, ensuring consistency and ecological relevance across scales by linking local trait-based PDE models with metapopulation ODE frameworks. 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.

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Directorate for Mathematical and Physical SciencesMATHEMATICAL BIOLOGY01002627DB NSF RESEARCH & RELATED ACTIVITbroadencontrolledtraitconservationdesigningdirecttradereproductivemissionmerit

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