A mechanistic simulation model of seed dispersal by animals


Correspondence author. E-mail: will@bio.uni-frankfurt.de


  • 1In order to investigate seed dispersal by animals on a landscape scale, we developed the spatially explicit, individual-based mechanistic model SEED (Simulation of Epi- and Endozoochorous Seed Dispersal). The purpose of the model is to predict patterns and densities of seeds dispersed by animals (especially mammals) within a simulated landscape.
  • 2The model was parameterized for sheep, cattle and deer as vectors but may be applied to other animals if data for parameterization is available. The model data base currently includes parameter values for about 100 plant species.
  • 3Seed attachment to and seed detachment from the fur, as well as seed excretion after passage through the gut, are explicitly simulated by drawing randomly from distributions that were determined by standardized experiments. Animal movement is simulated as a correlated random walk, but to increase reality of the model, radio-tracking data of animals can also be used.
  • 4A sensitivity analysis of SEED was conducted to identify the relative importance of plant and animal traits. The analysis highlighted where the main gaps in our knowledge of seed dispersal processes lie. Even though in our study endozoochorous dispersal had the higher potential for long-distance dispersal compared to epizoochory, there is only scarce knowledge about seed production and especially about the proportion of seeds eaten by an animal, parameters which were shown to be of major importance for dispersal.
  • 5A comparison of variation in plant and animal traits, respectively, showed that dispersal kernels depend more on changes in the animal vector than on the comparably little variation a particular plant species can exhibit. For this reason, animal movement is, from all the dispersal-relevant parameters, the one for which more exact data is most urgently needed.
  • 6Synthesis. The newly developed simulation model will help to understand, quantify and predict long-distance seed dispersal by animals. The possibility to incorporate real landscapes and movement data from very different animals makes the model generalizable and possibly applicable to a wide range of scientific and applied questions.