Seed dispersal near and far: Patterns across temperate and tropical forests

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.