EPSCoR Research Fellows: NSF: Linking Numerical and Metabolic Theories of Life History
Ente: EPSCoR RII: EPSCoR Research Fe
Scadenza: 2028-12-31
Importo max: 235.654 EUR
Paese: US
Descrizione
This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Kentucky. This work is conducted in collaboration with Dr. Shripad Tuljapurkar at Stanford University. Through the fellowship, the PI will investigate how organisms allocate energy to growth, survival, and reproduction across their life cycles. The research will examine why species that differ greatly in body size, lifespan, and metabolic rate can nevertheless achieve similar long-term reproductive success. To address this question, the project will advance a new scientific framework called the Equal Fitness Paradigm, which links metabolism of organisms to life history, population dynamics, and energy flows in ecosystems. By combining approaches from ecology, physiology, and population biology, the project will improve understanding of the fundamental processes that shape the diversity of life on Earth. The work will strengthen collaboration between Kentucky and Stanford researchers, train students in quantitative ecological methods, and develop open-source data tools that support research, education, and applied natural resource management.
This project will integrate numerical and metabolic theories of life history to advance the Equal Fitness Paradigm (EFP), a framework that links energetic constraints to life history evolution and population dynamics. The research will develop mathematical connections between metabolic scaling theory and matrix population models to quantify trade-offs among survival, growth, and reproduction across species. Using published databases for wild organisms, the project will establish computational workflows to standardize life history parameters and incorporate energetic variables for comparative analyses across taxa. These analyses will evaluate how energetic and numerical constraints produce common scaling relationships between energy use, generation time, and reproductive output resulting in approximately equally fit strategies. The fellowship will enhance research infrastructure by expanding faculty expertise in mathematical biology and strengthening collaboration between the University of Kentucky and Stanford University. Training activities will provide graduate students with advanced skills in population modeling, ecological theory, and data science. The resulting open-source datasets and analytical workflows will support future research and education in ecological theory, biodiversity science, and applied population studies. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundat
Istituzione: University of Kentucky Research Foundation
Sede: LEXINGTON, KY
PI: Joseph Burger
Settori: EPSCoR RII: EPSCoR Research Fe
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