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Long-distance seed dispersal is an important ecosystem service provided by migratory animals. Plants inhabiting discrete habitats, like lakes and wetlands, experience dispersal limitation and rely heavily on zoochory for their spatial population dynamics. Granivorous waterbirds may disperse viable seeds of wetland plants over long distances during migration. The limited knowledge of waterbird migration long hampered the evaluation of the importance of waterbirds in seed dispersal, requiring key metrics such as realistic dispersal distances. Using recent GPS tracks of mallards on spring migration, we built a mechanistic seed dispersal model to estimate realistic dispersal distances. Mallards are abundant, partially migratory ducks known to consume seeds of >300 European plant species. Based on the tracking data, we informed a mallard migration simulator to obtain a probabilistic spring migration model for the mallard population wintering at Lake Constance in Southern Germany. We combined the spring migration model with seed retention curves to develop seed dispersal kernels. We also assessed the effect of pre-migratory fasting and the availability of suitable deposition habitat for aquatic and wetland plants. Our results show that mallards at Lake Constance can disperse seeds in northeastern direction over median distances of 293 and 413 km for seeds with short and long retention times, respectively, assuming departure immediately after foraging. Pre-migratory fasting strongly affected the dispersal potential, with only 1-7% of ingested seeds left for dispersal after fasting for 12 h. Availability of suitable deposition habitat was generally <5% along the migratory flyway. High probability of seed deposition in freshwater habitat during the first stopover, after the mallards completed the first migratory flight, makes successful dispersal most likely to happen at 204-322 km from Lake Constance. We conclude that the directed long-distance dispersal of plant seed realized by mallards on spring migration may contribute significantly to large scale spatial plant population dynamics, including range expansion in response to shifting temperature and rainfall patterns under global warming. Our dispersal model is the first to incorporate detailed behavior of migratory waterbirds and can be readily adjusted to include other vector species when tracking data are available.