A Bayesian approach to model dispersal for decision support. (April 2016)

• Record Type: Journal Article
• Authors: Bensadoun, Arnaud; Monod, Hervé; Makowski, David; Messéan, Antoine
• Is Part Of: Environmental modelling & software. Volume 78(2016:Apr.)
• Page Start: 179
• Page End: 190
• Publication Date: 2016-04
• Subjects: Dispersal -- Variability -- Uncertainty -- Bayesian inference -- MCMC -- Decision support -- Risk assessment -- Sampling -- Zero-excess data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.12.018
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: 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.