Rapid Adaption of the Microbial Community to Abrupt Environmental Change in the Gulf of Mexico Modeled with the Genome-based EmergeNt Ocean Microbial Ecosystem Model

Microbial communities in the ocean are impacted by both natural and anthropogenic drivers (e.g. increasing temperature, ocean acidification, hypoxia, eutrophication, deep-water oil exploration, etc.). Understanding how microbial communities respond to major perturbations is central to understanding their role in modulating biogeochemical cycles as well as ecosystem resilience in a changing ocean. A new version of the Genome-based EmergeNt Ocean Microbial Ecosystem Model (GENOME Model) is adapted to include microbial communities that are able to degrade hydrocarbons in the Gulf of Mexico. The model is used to test how the communities respond to a major perturbation- the Deepwater Horizon (DwH) Oil Spill in the Gulf of Mexico, and how their activity influences local biogeochemical conditions (e.g. oxygen, nitrate). Hydrocarbon degradation by diverse simulated microbial communities is modeled as a function of the abundance of different hydrocarbon classes and the availability of potentially limiting energy and nutrient substrates. Genes encoding for hydrocarbon degradation processes relevant to the region are expressed in the model. The microbial communities in the model are initially equilibrated to hydrocarbon concentrations resulting from natural oil seeps, then the community is allowed to shift in response to the DwH Oil Spill. Microbial community structure associated with local environmental conditions are compared with in situobservations including metatranscriptomic data. The model simulates the lower species diversity, elevated cell density in the deep oil plume during the blow out that were previously reported. Realistic oxygen and nitrate depletions in the oil plume layer are also resolved by the model. This emergent, trait-based model allows us to assess the importance of microbial communities “primed” by hydrocarbons from natural seeps and to understand the resilience of microbial communities in response to major perturbations.