Methodological scheme for designing the monitoring of genetically modified crops at the regional scale.

According to EC regulations the deliberate release of genetically modified (GM) crops into the agro-environment needs to be accompanied by environmental monitoring to detect potential adverse effects, e.g. unacceptable levels of gene flow from GM to non-GM crops, or adverse effects on single species or species groups thus reducing biodiversity. There is, however, considerable scientific and public debate on how GM crops should be monitored with sufficient accuracy, discussing questions of potential adverse effects, agro-environmental variables or indicators to be monitored and respective detection methods; Another basic component, the appropriate number and location of monitoring sites, is hardly considered. Currently, no consistent GM crop monitoring approach combines these components systematically. This study focuses on and integrates spatial agro-environmental aspects at a landscape level in order to design monitoring networks. Based on examples of environmental variables associated with the cropping of Bt-Maize (Zea maize L.), herbicide-tolerant (HT) winter oilseed rape (Brassica napus L.), HT sugar beet (Beta vulgaris L.), and starch-modified potato (Solanum tuberosum L.), we develop a transferable framework and assessment scheme that comprises anticipated adverse environmental effects, variables to be measured and monitoring methods. These we integrate with a rule-based GIS (geographic information system) analysis, applying widely available spatial area and point information from existing environmental networks. This is used to develop scenarios with optimised regional GM crop monitoring networks.

Determining ecoregions for environmental and GMO monitoring networks.

A representative environmental monitoring network at the regional scale cannot use raster-based or random sampling designs, but requires a stratified sampling procedure integrating different information layers, and it has to occur in ecologically differing homogeneous regions (ecoregions). These we have determined using a set of spatial strata with ecological variables which we analysed with classification and regression trees (CART). We present a framework for environmental monitoring, that covers different scales, and we transfer the framework to a potential GMO (genetically modified organisms) monitoring network. We use ecoregion and other environmental strata together with existing environmental monitoring networks to determine GMO monitoring sites more precisely.