A Bayesian approach to model dispersal for decision support
Résumé
In agricultural and environmental sciences dispersal models are often used for risk assessment to predict
the risk associated with a given configuration and also to test scenarios that are likely to minimise those
risks. Like any biological process, dispersal is subject to biological, climatic and environmental variability
and its prediction relies on models and parameter values which can only approximate the real processes.
In this paper, we present a Bayesian method to model dispersal using spatial configuration and climatic
data (distances between emitters and receptors; main wind direction) while accounting for uncertainty,
with an application to the prediction of adventitious presence rate of genetically modified maize (GM) in
a non-GM field. This method includes the design of candidate models, their calibration, selection and
evaluation on an independent dataset. A group of models was identified that is sufficiently robust to be
used for prediction purpose. The group of models allows to include local information and it reflects
reliably enough the observed variability in the data so that probabilistic model predictions can be performed and used to quantify risk under different scenarios or derive optimal sampling schemes.