Dispersal affects community dynamics and vegetation response to global chan
ge. Understanding these effects requires descriptions of dispersal at local
and regional scales and statistical models that permit estimation. Classic
al models of dispersal describe local or long-distance dispersal, but not b
oth. The lack of statistical methods means that models have rarely been fit
ted to seed dispersal in closed forests. We present a mixture model of disp
ersal that assumes a range of disperal patterns, both local and long distan
ce. The bivariate Student's t or "2Dt" follows from an assumption that the
distance parameter in a Gaussian model varies randomly, thus having a densi
ty of its own. We use an inverse approach to "compete" our mixture model ag
ainst classical alternatives, using seed rain databases from temperate broa
dleaf, temperate mixed-conifer, and tropical floodplain forests. For most s
pecies, the 2Dt model fits dispersal data better than do classical models,
The superior fit results from the potential for a convex shape near the sou
rce tree and a "fat tail." Our parameter estimates have implications for co
mmunity dynamics at local scales, for vegetation responses to global change
at regional scales, and for differences in seed dispersal among biomes. Th
e 2Dt model predicts that less seed travels beyond the immediate crown infl
uence (<5 m) than is predicted under a Gaussian model, but that more seed t
ravels longer distances (>30 m). Although Gaussian and exponential models p
redict slow population spread in the face of environmental change, our disp
ersal estimates suggest rapid spread. The preponderance of animal-dispersed
and rare seed types in tropical forests results in noisier patterns of dis
persal than occur in temperate hardwood and conifer stands.