Award: OCE-1325452

Intellectual Merit: Our project leveraged fisheries ?big data? on the spatial behavior of fishing fleets to improve the scientific basis of resource management decisions in the Gulf of Mexico reef fish fisheries. We focused on understanding how Gulf of Mexico fishery participants respond to disturbances such as spatial management interventions (e.g., fishery closures) and the BP Deepwater Horizon oil spill, which was the largest environmental disaster in U.S. history. Specifically, we developed machine-learning-based methods to map where and when fishing is taking place, achieving far higher spatial resolution and accuracy than was previously possible in the Gulf (Figure 1). Adopting techniques from geospatial analysis, we investigated how the spatial scale chosen for policy and welfare analysis affects outcomes, allowing us to make recommendations on developing robust methods for modeling fishing decision-making. Using empirically grounded models, we then simulated how fishing-ground choice was likely to be affected by the implementation of marine protected areas. We examined a suite of factors that affected the likelihood of vessels being forced to retire from fishing as a result of major disturbance events, taking advantage of the high-resolution movement data to provide unprecedented insight to impacts, such as quantifying how much fishing ground each vessel lost as a result of the disturbances, and enabling us to pinpoint which attributes predict vulnerability. We used the linked datasets to characterize fishing behavioral traits of vessels, such as the tendency to explore new fishing locations, and determined how these traits can affect the impacts of disturbances. Our novel use of vessel movement data to inform measures of vulnerability holds promise for improving management outcomes for those most adversely affected by economic, environmental or policy changes. Broader Impacts: Our project has addressed a key problem in fisheries science, namely how fishing fleets respond to spatial disturbances such as the implementation of marine protected areas and management efforts to mitigate oil spill impacts. We have developed tools for fisheries scientists to extract important new information from existing datasets in understanding and predicting fishing behavior in the Gulf of Mexico and elsewhere. Our research has allowed us to make a series of evidence-based recommendations for improving the ways in which fisheries data are gathered, processed and analyzed. These research outcomes will allow fisheries management information systems to capitalize on the high-resolution datasets being gathered in the Gulf and elsewhere in the US. Last Modified: 01/15/2018 Submitted by: James N Sanchirico