This research will advance our understanding of how evolutionary changes happening within populations (microevolution) drive the accumulation of genetic barriers to reproduction between populations, eventually resulting in the origin of new species (macroevolution). Although it is well established that differences in the environment experienced by diverging populations can result in different adaptations that are not compatible in the hybrid offspring of divergent parents, only a few evolution experiments have shown that the same can happen in populations evolving in identical environments. The aim of this research is to develop mathematical models for predicting the rate at which incompatible adaptations accumulate between populations evolving in identical environments, and to test the predictions using laboratory evolution experiments. The project will build on a 14-year collaboration between the researchers (Burch and Azevedo) that has used a combination of computational and laboratory evolution experiments to investigate the causes and consequences of interactions between genes. In addition, the project will capitalize on the connections that the researchers have already formed with Latino organizations and dual-language primary schools in and around Chapel Hill and Houston to build teams of primarily Latino students. The researchers will mentor the student teams to develop games and activities to teach basic concepts in coding, mathematical modeling, microbiology, and evolution, that the teams will then deliver, in English and Spanish, to classrooms and science expos in these communities. The goal is for the students to become teachers over the course of this outreach project.
The project will use computational and laboratory evolution experiments to investigate the initial stages of speciation, from the accumulation of the very first incompatible adaptations. The central goals will be to determine, experimentally, whether adaptation often results in the accumulation of functionally interacting mutations that, over time, make it difficult for diverging populations to produce viable hybrid offspring. Computer simulations and mathematical models will be used to develop quantitative predictions that will then be tested using laboratory evolution experiments. The researchers will: 1) monitor the evolution of replicate populations of the bacteriophage T7 evolving in identical laboratory environments; 2) generate hybrid offspring between pairs of these evolving populations and monitor the accumulation of mutations that increase fitness in one of the parental populations, but decrease fitness in their hybrid offspring, 3) measure the rate at which such incompatible adaptations accumulate, and 4) determine whether the identified incompatible adaptations resulted from the kinds of functional interactions that the researchers? mathematical models predict to be likely.
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.
