How to identify the drivers of population connectivity remains a fundamental question in ecology and evolution. Answering this question can be challenging in aquatic environments where dynamic lake and ocean currents coupled with high levels of dispersal and gene flow can decrease the utility of modern population genetic tools. To address this challenge, we used RAD-Seq to genotype 959 yellow perch (Perca flavescens), a species with an ~40-day pelagic larval duration (PLD), collected from 20 sites circumscribing Lake Michigan. We also developed a novel, integrative approach that couples detailed biophysical models with eco-genetic agent-based models to generate “predictive” values of genetic differentiation. By comparing predictive and empirical values of genetic differentiation, we estimated the relative contributions for known drivers of population connectivity (e.g., currents, behavior, PLD). For the main basin populations (i.e., the largest contiguous portion of the lake), we found that high gene flow led to low overall levels of genetic differentiation among populations (F_ST = 0.003). By far the best predictors of genetic differentiation were connectivity matrices that were derived from periods of time when there were strong and highly dispersive currents. Thus, these highly dispersive currents are driving the patterns of population connectivity in the main basin. We also found that populations from the northern and southern main basin are slightly divergent from one another, while those from Green Bay and the main basin are highly divergent (F_ST = 0.11). By integrating biophysical and eco-genetic models with genome-wide data, we illustrate that the drivers of population connectivity can be identified in high gene flow systems.