Dispersal is important for determining both a species ecological processes, such as population viability, and its evolutionary processes, like gene flow and local adaptation. Yet obtaining accurate estimates in the wild through direct observation can be challenging or even impossible, particularly over large spatial and temporal scales. Genotyping many individuals from wild populations can provide detailed inferences about dispersal. We therefore utilized genomewide marker data to estimate dispersal in the classic metapopulation of the Glanville fritillary butterfly (Melitaea cinxia L.), in the Åland Islands in SW Finland. This is an ideal system to test the effectiveness of this approach due to the wealth of information already available covering dispersal across small spatial and temporal scales, but lack of information at larger spatial and temporal scales. We sampled three larvae per larval family group from 3,732 groups over a six-year period and genotyped for 272 SNPs across the genome. We used this empirical dataset to reconstruct cases where full-sibs were detected in different local populations to infer female effective dispersal distance, i.e. dispersal events directly contributing to gene flow. On average this was one kilometer, closely matching previous dispersal estimates made using direct observation. To evaluate our power to detect full-sib families we performed forward simulations using an individual-based model constructed and parameterized for the Glanville fritillary metapopulation. Using these simulations 100% of predicted full-sibs were correct and over 98% of all true full-sib pairs were detected. We therefore demonstrate that even in a highly dynamic system with a relatively small number of markers, we can accurately reconstruct full-sib families and for the first time make inferences on female effective dispersal. This highlights the utility of this approach in systems where it has previously been impossible to obtain accurate estimates of dispersal over both ecological and evolutionary scales.