Modeling of gene flow by a Bayesian approach: A new perspective for decision support
Monod, Herve | Angevin, Frederique | Makowski, David | Messean, Antoine
In the European debate about GMOs, the coexistence betw een GM and nonGM crops is a major stake. The regulatory coexistence measures presently considered by member states mostly rely on fixed separation distances at a national scale. Several spatially explicit modelling approaches have been studied to help determine these separation distances. However the formalism used in those models and the availibility of relevant and independant data for calibration and validation make the uncertainty analysis of those models almost impossible. The study presented here aims at developing an alternative modelbased approach with emphasis on uncertainty to better adapt coexistence rules to any specific situation. The research work focuses on the use of bayesian methods to design a collection of statistical models at the scale of an agricultural landscape. Those models yield crosspollination rate in nonGM fields and are flexible enough to adapt to the available in situ information. Thanks to the bayesian approach, estimates are computed as distributions whose dispersion depends on the amount and quality of available data; the more abundant and accurate the data, the narrower the distribution. In addition to model construction, we propose a coherent approach to select the best model for a given situation. The selection does not only rely on goodness of fit but also on the quality of the resulting decision for a given threshold. Models are compatible with the DST of the EU project PRICE.
Показать больше [+] Меньше [-]