Networks of mutualistic interactions are key for the creation and maintenance of biodiversity and generate multiple ecological functions and services. Our understanding of mutualistic networks has been revolutionized over the last 15 years but theoretical and empirical approaches to understanding mutualistic networks have largely progressed in isolation, with very little integration between the two approaches. Many potential avenues of integration pose clear difficulties, for example empirical parameterization of pairwise interaction strengths in models is intractable in even the simplest communities, and empirical assessment or testing of many model outputs (such as long-term species persistence or robustness to coexistence) are essentially impossible. Recently, however, a few studies have taken steps to integrate empirical and theoretical approaches, both in terms of 1) using empirical data and patterns to inform, structure, and parameterize mutualistic networks; and 2) testing patterns generated by models with empirical data. Much of the integrative work the symposium will cover has an explicit focus on temporal scaling, especially given that long-term equilibria predicted by models can rarely (if ever) be tested in non-microbial systems. In addition, work on food webs has a longer history than mutualistic networks, including a much more substantial tradition of integrating theory and empirical work. The symposium will include a speaker whose work has focused on that integration in food webs, and all speakers will incorporate perspectives from other ecological networks as well as ways that better empirical-theoretical integration in mutualistic networks could inform other systems. In summary, this symposium will highlight cutting-edge work that integrates both theory and empirical data to increase our understanding of mutualistic networks. This includes work specifically involving both perspectives as well as empirical work that identifies key gaps in modeling, and theory work that identifies key gaps in empirical studies and data collection.