The Deepwater Horizon oil spill was the largest accidental marine oil spill in history and affected the benthic ecosystems, as well as vast areas of the open ocean and coastal wetlands along the Gulf of Mexico. Biodegradation mediated by a complex network of
microorganisms dictates the ultimate fate of the majority of oil hydrocarbons that enter the marine environment. There is a fundamental lack of baseline environmental data and understanding of the rate of microbial oil degradation that could be used to formulate effective responses to an environmental disaster of this magnitude. Previous models only focused on culture based microbial techniques, but these microbes make up less than 1% of these environmental systems, thus leaving a vast majority of the “microbial dark matter” unexplored. The ecosystem level interactions that dictate microbial community structuring are highly complex and culture independent DNA/RNA analyses can help unravel these complex interactions. We leveraged terabytes of microbial “omics” data (which harnesses the power of computational biology and machine learning) along with engineered “real-time” systems to produce oil degradation models that can help environmental managers with future oil spill response plans. Furthermore, we curated a comprehensive and searchable database documenting microbial indicators that responded to accidental or natural oil spills across a range of global ecosystems along with their underlying physicochemical data, geocoded via GIS to reveal their biogeographic distribution patterns. This interactive repository can help provide a predictive understanding of the microbial response to oil perturbations and identify biomarkers that can universally predict ecosystem recovery.