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Effective management of social-ecological systems (SESs) requires an understanding of human behavior. In many SESs, there are hundreds of agents or more interacting with governance and regulatory institutions, driving management outcomes through collective behavior. Agents in these systems often display consistent behavioral characteristics over time that can help reduce the dimensionality of SES data by enabling the assignment of types. Typologies of resource-user behavior both enrich our knowledge of user cultures and provide critical information for management. Here, we develop a data-driven framework to identify resource-user typologies in SESs with high-dimensional data. To demonstrate policy applications, we apply the framework to a tightly coupled SES, commercial fishing. We leverage large fisheries-dependent datasets that include mandatory vessel logbooks, observer datasets, and high-resolution geospatial vessel tracking technologies. We first quantify vessel and behavioral characteristics using data that encode fishers’ spatial decisions and behaviors. We then use clustering to classify these characteristics into discrete fishing behavioral types (FBTs), determining that 3 types emerge in our case study. Finally, we investigate how a series of disturbances applied selection pressure on these FBTs, causing the disproportionate loss of one group. Our framework not only provides an efficient and unbiased method for identifying FBTs in near real time, but it can also improve management outcomes by enabling ex ante investigation of the consequences of disturbances such as policy actions. 

Abstract 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.