Dispersal affects community dynamics and vegetation response to global change. Understanding these effects requires descriptions of dispersal at local and regional scales and statistical models that permit estimation. Classical models of dispersal describe local or long-distance dispersal, but not both. The lack of statistical methods means that models have rarely been fitted to seed dispersal in closed forests. We present a mixture model of dispersal that assumes a range of disperal patterns, both local and long distance. The bivariate Student’s t or “2Dt” follows from an assumption that the distance parameter in a Gaussian model varies randomly, thus having a density of its own. We use an inverse approach to “compete” our mixture model against classical alternatives, using seed rain databases from temperate broadleaf, temperate mixed-conifer, and tropical floodplain forests. For most species, the 2Dt model fits dispersal data better than do classical models. The superior fit results from the potential for a convex shape near the source tree and a “fat tail.” Our parameter estimates have implications for community dynamics at local scales, for vegetation responses to global change at regional scales, and for differences in seed dispersal among biomes. The 2Dt model predicts that less seed travels beyond the immediate crown influence (<5 m) than is predicted under a Gaussian model, but that more seed travels longer distances (>30 m). Although Gaussian and exponential models predict slow population spread in the face of environmental change, our dispersal estimates suggest rapid spread. The preponderance of animal-dispersed and rare seed types in tropical forests results in noisier patterns of dispersal than occur in temperate hardwood and conifer stands.