An evolutionary landscape to better predict our future climate. Soil microbial communities are the most complicated and difficult to study on Earth, but their effects on our climate are profound. This project will examine the evolution of microorganisms and their viruses in soil using novel methods. It will uncover how the evolution of one microbial species influences the evolution of other community members. It will also apply a new model of evolution to the viruses that infect these microorgan ....An evolutionary landscape to better predict our future climate. Soil microbial communities are the most complicated and difficult to study on Earth, but their effects on our climate are profound. This project will examine the evolution of microorganisms and their viruses in soil using novel methods. It will uncover how the evolution of one microbial species influences the evolution of other community members. It will also apply a new model of evolution to the viruses that infect these microorganisms, constructing a viral ‘tree of life’. This improved fundamental understanding of soil communities will be used to study climate feedback from permafrost wetlands, a key and poorly constrained input of global climate models, improving predictions of our future climate.Read moreRead less
Global integration of microbial community and climate data. Microbial communities in the environment control the cycling of carbon and nutrients on Earth, but climate models do not directly incorporate microbial inputs. This interdisciplinary project will link planetary-scale climate modelling data with novel large-scale microbial community analysis, using climate information to provide insight into the fantastic diversity of microbial processes on our planet. The interdisciplinary approach will ....Global integration of microbial community and climate data. Microbial communities in the environment control the cycling of carbon and nutrients on Earth, but climate models do not directly incorporate microbial inputs. This interdisciplinary project will link planetary-scale climate modelling data with novel large-scale microbial community analysis, using climate information to provide insight into the fantastic diversity of microbial processes on our planet. The interdisciplinary approach will inform the next generation of climate models and better predict our future climate’s feedbacks. Conversely, it will make progress on the grand challenge of understanding microbial community function by enabling microbial ecology to be treated as a data-intensive machine learning problem.Read moreRead less