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Abstract ObjectiveFisheries provide countless benefits to human populations but face many threats ranging from climate change to overfishing. Despite these threats and an increase in fishing pressure globally, most stocks remain unassessed and data limited. An abundance of data-limited assessment methods exists, but each has different data requirements, caveats, and limitations. Furthermore, developing informative model priors can be difficult when little is known about the stock, and uncertain model parameters could create misleading results about stock status. Our research illustrates an approach for rapidly creating robust initial assessments of unregulated and data-limited fisheries without the need for additional data collection. MethodsOur method uses stakeholder knowledge combined with a series of data-limited tools to identify an appropriate stock assessment method, conduct an assessment, and examine how model uncertainty influences the results. Our approach was applied to the unregulated and data-limited fishery for Crevalle Jack Caranx hippos in Florida. ResultResults suggested a steady increase in exploitation and a decline in stock biomass over time, with the stock currently overfished and undergoing overfishing. These findings highlight a need for management action to prevent continued stock depletion. ConclusionOur approach can help to streamline the initial assessment and management process for unregulated and data-limited stocks and serves as an additional tool for combating the many threats facing global fisheries. 

Abstract Atlantic bluefin tuna (ABT) (Thunnus thynnus) travel long distances to spawn in oligotrophic regions of the Gulf of Mexico (GoM) which suggests these regions offer some unique benefit to offspring survival. To better understand how larval survival varies within the GoM a spatially explicit, Lagrangian, individual-based model was developed that simulates dispersal and mortality of ABT early life stages within realistic predator and prey fields during the spawning periods from 1993 to 2012. The model estimates that starvation is the largest cumulative source of mortality associated with an early critical period. However, elevated predation on older larvae is identified as the main factor limiting survival to late postflexion. As a result, first-feeding larvae have higher survival on the shelf where food is abundant, whereas older larvae have higher survival in the open ocean with fewer predators, making the shelf break an optimal spawning area. The modeling framework developed in this study explicitly simulates both physical and biological factors that impact larval survival and hence could be used to support ecosystem based management efforts for ABT under current and future climate conditions.