The study of the prehistoric origins and dispersal routes of domesticated plants is often based on the analysis of either archaeobotanical or genetic data. As more data become available, spatially explicit models of crop dispersal can be used to combine different types of evidence.
We present a model in which a crop disperses through a landscape that is represented by a conductance matrix. From this matrix, we derive least-cost distances from the geographical origin of the crop and use these to predict the age of archaeological crop remains and the heterozygosity of crop populations. We use measures of the overlap and divergence of dispersal trajectories to predict genetic similarity between crop populations. The conductance matrix is constructed from environmental variables using a number of parameters. Model parameters are determined with multiple-criteria optimization, simultaneously fitting the archaeobotanical and genetic data. The consilience reached by the model is the extent to which it converges around solutions optimal for both archaeobotanical and genetic data. We apply the modelling approach to the dispersal of maize in the Americas.
The approach makes possible the integrative inference of crop dispersal processes, while controlling model complexity and computational requirements.