CAREER: The effects of spatial structure and heterogeneity on local adaptation, diversification, and dispersal evolution: Experimental tests and statistical models

Most species live in complex and variable environments. Variation in within an environment impacts where species live, how they move around, and how they interact with each other. This, in turn, can influence how they evolve.

Understanding and predicting evolution in complex environments has many important applications. These include the management of threatened species and combating rapidly evolving pathogens such. This project uses mathematical models and lab experiments with bacteria to explore how variation in an environment impacts evolution.

Results from this work will allow for a better understanding of how and when evolution is predictable. The project also provides research training for undergraduate and graduate students, expanded undergraduate curriculum, as well as an enrichment course for rural high school students emphasizing important connections between biology and math.

In the complex natural world, environmental variation and local interactions between organisms have the potential to play a key role in evolutionary adaptation and diversification. The goal of this project is to determine the effects of spatially structured and variable environments on local adaptation, evolutionary diversification, and dispersal using evolution experiments with populations of the bacterium, Pseudomonas fluorescens.

First, agent-based models will be developed and used to build expectations for evolution of populations living in different types of spatially heterogeneous environments. Then, using those expectations as a guide, the effects of spatial heterogeneity on the evolution of those bacterial populations will be tested using a unique semi-solid agar lab environment.

Genome sequence data will be collected and then be analyzed using novel statistical models of molecular evolution. The population genome data analysis will provide a unique first look at the details of molecular evolution in spatially structured populations. Results from this research will lead to a better understanding of the processes driving evolution in complex environments, ultimately allowing for better predictive models of evolution in species of economic, environmental, and human health importance.

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.