This INSPIRE award is partially funded by the Ecosystem Studies Program in the Division of Environmental Biology in the Directorate for Biological Sciences and the Environmental Engineering Program in the Division of Chemical, Bioengineering, Environmental, and Transport Systems in the Directorate for Engineering.
Microbes are critical, but often ignored, players in all ecosystems. They have tremendous influence on ecosystem resilience, with implications for predicting the response and feedback to drivers ranging from climate change to land-use change and management. However, it has been challenging to incorporate microbes and their vast diversity into predictive environmental models. Specifically, key advances are needed to address the knowledge gaps that prevent integration of microbial genomic information into ecosystem-scale science. Solutions will be highly interdisciplinary, with contributions from microbiologists, ecologists, computational biologists, and engineers. This project aims to use genomic data to determine how microbes in freshwater lakes control carbon cycling and water quality, and to convert this new understanding into predictive computational models that forecast how lakes will behave. Environmental engineers have a long history of working quantitatively with water quality models, but they do not incorporate microbes into them. Ecosystem scientists study the flux of matter and energy through ecosystems but they rarely consider microbes and often do not construct models based on first principles. This work will bring together three disciplines that are rarely integrated: microbiology, ecosystem science, and environmental engineering.
This work will have broad impacts on science as well as education and outreach efforts. Microbial biologists and ecologists are hungry for new ways to transform complex datasets and fundamental concepts into understanding and prediction. Students and post-docs in fields of microbiology, ecology, and engineering need cross-training in computational biology, genomics, and modeling. They will need to push their science to be less descriptive and more predictive, which requires computational and integrative thinking that transcends the structure of current graduate programs in these disciplines. The project will leverage relevant network science initiatives such as the Global Lake Ecological Observatory Network (GLEON) and the nascent Genomic Observatories (GOs) network. Finally, our ability to make microbial ecosystem science relevant to the broader public depends on illustrating how tiny entities can have large impact on high-value ecosystems services. Freshwater lakes are excellent systems for this purpose since water quality is so tightly linked to microbes, with major feedbacks to local economic activity, ecosystem management decisions, and public perception of ecosystem health.
