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NSF
The survival of coral reef ecosystems depends on their capacity to adapt to rapidly warming oceans. This project aims to understand and predict these critical adaptation processes by developing predictive tools for coral reef conservation while creating educational pathways in marine science. Through workshops, internships, and public education programs at the Hawaiʻi Institute of Marine Biology, this research engages students and community members in understanding how corals may adapt to changing environments. These educational initiatives focus on student mentorship and training, while research outcomes directly inform conservation strategies to protect reef ecosystems for future generations. This research aims to develop a mechanistic theoretical framework that models coral adaptation by integrating three key mechanisms: larval connectivity between reefs, genetic adaptation of coral hosts, and dynamics of their symbiotic algae. The project will construct a series of mathematical models spanning multiple scales, from within-host symbiont communities to reef-network connectivity patterns. These models will quantify how different coral and symbiont characteristics influence adaptation patterns across reef networks under various climate scenarios. The framework can be applied to Hawaiian coral reefs using local oceanographic data to assess their adaptive potential through 2100. Model predictions aim to identify vulnerable coral species and reef areas, as well as potential refugia. By revealing the key mechanisms driving coral adaptation across scales, this work will provide critical insights for coral reef conservation under rapid environmental change. This project is supported by the Biological Oceanography Program in the Division of Ocean Sciences and the Mathematical Biology Program in the Division of Mathematical Sciences. 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.
Up to $1000K
2030-06-30
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