Risk indication of genetically modified organisms (GMO): Modelling environmental exposure and dispersal across different scales Oilseed rape in Northern Germany as an integrated case study

摘要

Ecological indication is the most relevant way to approximate the implications of cause-effect networks which go beyond spatio-temporal extents of direct experimental accessibility. Risk analysis and risk management of genetically modified plants are an application field where indication of potential effects on the landscape and regional scale is required. Long-term implications of commercial use can be assessed only to a limited extent through direct experimental approaches. Landscapes and regions normally cannot be subjected to experimental manipulation. However, empirical results obtained on smaller scales can help to indicate long term, delayed and combinatory effects to some extent when an appropriate up-scaling procedure of small-scale and short-term results is developed. Using oilseed rape cultivation in Northern Germany as an example, it is shown, how a model-based integration of known effects can be used to understand large-scale implications. The indication approach combines remote sensing data, weather data, biogeographic data, and model simulation of local interactions. Validated knowledge starting on the level of individual plants and plant populations was used. On the basis of state-of-the-art knowledge, the geo-statistical approach is outlined, how to draw conclusions for processes up to the regional scale. In this paper, we present an overview, which steps are necessary to gain a coherent picture. Each of the involved steps, representing a contribution from a different disciplinary and methodological background and operating on different scales, is documented in further details in the papers collated in this special issue. This introductory contribution to the special issue outlines, what the involved steps are and how they combine to produce the overall results. It was demonstrated, that local interactions aggregate in a non-trivial way. The understanding of regional cultivation density implications could be improved with an approach that integrated local information through model scenario calculations.