Intact food webs underlie the sustainability of fisheries worldwide. Our approach to understanding food webs is undergoing a rapid expansion of ideas and approaches. These innovative approaches include new elemental and chemical biomarker techniques to identify poorly understood trophic connections, community based models to understand interactions among species, whole ecosystem models to understand ecosystem-level drivers of food web dynamics, and social network analytical methods to characterize and model interactions between humans and fisheries. Fundamentally, fisheries are humans harvesting animals from aquatic systems, and humans collaborate and compete to harvest fish – they share (or fail to share) information about the locations where fishing is best, innovate harvest methods, and have highly overlapping economic strategies. Fishing regulations impact the food web indirectly by impacting the human behavior. Humans are also responsible for the dispersal of invasive species (e.g, dreissenids, lionfish). These human behavioral interactions can be modeled as well as the ecosystem species interactions using coupled ecological social network models.  These modeling efforts have the potential to reveal how changes in any one part of these coupled systems can impact seemingly unrelated elements of the overall system, as, for example, in both the direct and indirect effects of fisheries regulations on not only human behavior but also food web and supply chain dynamics.  We invite contributions to this session that presents new methodologies for characterizing, assessing, and quantifying aquatic food web networks across a range of ecosystem types and spatial and temporal scales. We anticipate that these food web approaches are being applied to address emerging problems that affect food web functionality, such as new fishing methods and innovations, regulatory impacts, marine protected areas, climate change, cultural eutrophication, invasive species, and over-fishing. This symposium will bring together social and natural scientists using a common analysis and modeling technique – network analysis.