CAREER: Harnessing Emergent Simplicity for High-Precision Predictions in High-Diversity Microbial Ecosystems

Microbial communities play a defining role in global climate, agriculture, food safety, and environmental health. Many of their practically relevant properties (for example, rate of consumption of a contaminant) result from the interactions of many species, making them challenging to predict even in the simplified laboratory conditions. Natural systems are even more complex, often composed of hundreds of interacting species, and one expects predictions to be even more difficult. However, recent evidence shows that, at least in some cases, communities with many species may in fact obey simpler "emergent" relationships. This project will develop a body of theory explaining this empirical observation; use methods of physics to classify the kinds of emergent simplification that ecosystem properties can exhibit; adapt this understanding into a methodology for disentangling the biological mechanism of emergent community function, and validate this methodology in a model complex ecosystem (minimally processed agricultural soil). The research is integrated with educational activities integrating curriculum changes and high school outreach, aiming to rebrand the Physics major as the intellectual home for students passionate about working with highly quantitative real-world data. This will be achieved by creating an integrated three-level set of courses (for high school students, freshmen and advanced undergraduates), built on common library of data-driven projects that foster curiosity and focus on relatable real-life questions, and collaborating with high school teachers in the St. Louis area to integrate “Physicists as data wizards” angle into lesson plans at grades 9-10. 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.