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Award Detail

Doing Business As Name:University of North Carolina at Chapel Hill
  • Joel G Kingsolver
  • (919) 843-6291
Award Date:01/13/2020
Estimated Total Award Amount: $ 297,174
Funds Obligated to Date: $ 297,174
  • FY 2020=$297,174
Start Date:05/01/2020
End Date:04/30/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.074
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:OPUS: CRS: Phenotypic selection in nature: Analysis and synthesis
Federal Award ID Number:1950055
DUNS ID:608195277
Parent DUNS ID:142363428
Program:Evolutionary Processes
Program Officer:
  • Leslie J. Rissler
  • (703) 292-4628

Awardee Location

Street:104 AIRPORT DR STE 2200
County:Chapel Hill
Awardee Cong. District:04

Primary Place of Performance

Organization Name:University of North Carolina at Chapel Hill
Street:Department of Biology CB-3280
City:Chapel Hill
County:Chapel Hill
Cong. District:04

Abstract at Time of Award

Natural selection is the primary evolutionary mechanism that adapts organisms to their environments, but the general patterns of selection in natural populations remain poorly understood. In particular, biologists lack a framework for connecting environmental change to changes in selection in the wild. The goal of this project is to develop such a framework, by integrating empirical and experimental data from field studies of insect populations with mathematical modeling and new statistical methods. The approach will be used to evaluate how recent environmental change has changed patterns of selection in insect populations. The general framework will provide new tools for understanding how habitat destruction, pollution, and other aspects of environmental change will alter natural selection in populations in the wild—and whether natural populations can keep up with human-induced environmental changes. The project will also synthesize and analyze the published scientific literature on selection in the wild, and provide research training in biology, data science, and statistical modeling for graduate students. Phenotypic selection is a primary mechanism that drives evolutionary change and adaptation, but biologists lack a quantitative framework that connects phenotype, environment, fitness and selection in natural populations. The proposed research will develop such a framework using the underlying data from field studies of selection in insect populations obtained over the past 30+ years. The studies focus on three general themes: integrating information from studies of natural variation, experimental manipulations of phenotype and environment, and functional models to quantify non-linear fitness surfaces and the resulting patterns of selection; applying new statistical models for estimating selection on reaction norms, performance curves, and growth trajectories; and modeling the relationship between environmental change and changes in the fitness surface. The project will also update existing datasets for standardized selection coefficients, and provide new meta-analyses to evaluate the current evidence for stabilizing and disruptive selection in the field, and whether variation in selection is associated with variation in climate and other environmental factors. The findings from these studies will be summarized in a research monograph on phenotypic selection in the wild, and the data, associated metadata, and R code for the analyses will be made openly available in an appropriate repository. 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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