A Bayesian approach is proposed to model dispersal and to make probabilistic predictions which account for uncertainty.

16 statistical gene flow models were designed, calibrated and compared within the Bayesian framework.

Models with Zero-inflated Poisson distribution and with exponential decay turn out to provide the most reliable predictions.

The proposed approach allows to set up context-specific isolation distances by providing accurate probabilistic predictions.

Thanks to precise predictions of intra-field variability, our models allow to design optimal stratified sampling schemes.