A methodological approach of site selection and data analysis to provide model input data for an up-scaling of population effects of transgenetic oilseed rape in Northern Germany

The joint research project “Generic detection and extrapolation of genetically modified rape (GenEERA)” aims at estimating the dispersal and persistence of rape (Brassica napus). This is executed by using remote sensing data, individual-based models, as well as geoinformation systems (GIS) techniques and statistical procedures. The two latter tasks are objectives of the “GenEERA-Regional Study Lower Saxony (GenEERA-RSLS)”. By means of GIS and statistics, the spatial extrapolation of the model results on a landscape level are possible in the form of geoscientific analogical reasoning. The article treats the concept, the statistical preparation and analysis of the data needed for the modelling and the spatial extrapolation of the modelling results as well as the selection of sites subjected to the individual-based modelling. This selection takes place according to the representativeness of all eligible monitoring sites in the German Federal States of Brandenburg (Berlin included), Mecklenburg–Vorpommern, Lower Saxony (Bremen included) and Schleswig–Holstein (Hamburg included) with regard to those factors which are important for the dispersal and persistence of rape: spreading and beginning of rape blossom, wind direction and wind intensity. Data on climate and ecoregions are also considered. As a result, this leads to a list of ecologically representative sites, which are to be suggested for the individual-based modelling. The modelling results are to be transferred later with GIS techniques and statistic procedures by analogy reasoning to areas for which the modelling sites are representative. However, modelling, site selection and the extrapolation of modelling results are operating on incomplete empirical knowledge and database, which can only be improved by way of statistically founded monitoring of the dispersal and ecological impacts effect of genetically engineered plants (GEP) in the environment.