Global integration of microbial community and climate data

环境中的微生物群落控制着地球上碳和养分的循环，但气候模型并不直接考虑微生物的输入。这个跨学科项目将把行星尺度的气候模拟数据与新型的大规模微生物群落分析联系起来，利用气候信息来深入了解我们星球上微生物过程的奇妙多样性。跨学科的方法将为下一代气候模型提供信息，并更好地预测我们未来的气候反馈。相反，通过将微生物生态学视为数据密集型机器学习问题，它将在理解微生物群落功能的重大挑战上取得进展。

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.