Major oil spills immensely impact the environment and society. Coastal fishery-dependent communities are especially at risk as their fishing grounds are susceptible to closure because of seafood contamination threat. During the Deepwater Horizon (DWH) disaster for example, vast areas of the Gulf of Mexico (GoM) were closed for fishing, resulting in coastal states losing up to a half of their fishery revenues. To predict the effect of future oil spills on fishery-dependent communities in the GoM, we develop a novel framework that combines a state-of-the-art three-dimensional oil-transport model with high-resolution spatial and temporal data for two fishing fleets—bottom longline and bandit-reel—along with data on the social vulnerability of coastal communities. We demonstrate our approach by simulating spills in the eastern and western GoM, calibrated to characteristics of the DWH spill. We find that the impacts of the eastern and western spills are strongest in the Florida and Texas Gulf coast counties respectively both for the bandit-reel and the bottom longline fleets. We conclude that this multimodal spatially explicit quantitative framework is a valuable management tool for predicting the consequences of oil spills at locations throughout the Gulf, facilitating preparedness and efficient resource allocation for future oil-spill events.

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Igal Berenshtein, Shay O’Farrell, Natalie Perlin, James N Sanchirico, Steven A Murawski, Larry Perruso, Claire B Paris, Predicting the impact of future oil-spill closures on fishery-dependent communities—a spatially explicit approach, ICES Journal of Marine Science, Volume 76, Issue 7, December 2019, Pages 2276–2285, https://doi.org/10.1093/icesjms/fsz138

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