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Purpose: Feral oilseed rape has become widespread in Europe on waysides and waste ground. Its potential as a source of GM impurity in oilseed rape harvests is quantified, for the first time, by a consistent analysis applied over a wide range of study areas in Europe., Methods: The maximum contribution of feral oilseed rape to impurities in harvested crops was estimated by combining data on feral abundance and crop yield from five established, demographic studies in agricultural habitats in Denmark, Germany (2), France and the UK, constituting over 1,500 ha of land and 16 site-years of observations. Persistence of feral populations over time was compared by visual and molecular methods., Results: Ferals had become established in all regions, forming populations 0.2 to 15 km⁻². The seed they produced was always <0.0001% of the seed on crops of oilseed rape in each region. The contribution of ferals to impurity in crops through accidental harvest of seed and through cross-pollination would be an even smaller percentage. Feral oilseed rape nevertheless showed a widespread capacity to persist in all regions and retain traits from varieties no longer grown., Conclusions: Feral oilseed rape is not a relevant source of macroscopic impurity at its present density in the landscape but provides opportunity for genetic recombination, stacking of transgenes and the evolution of genotypes that under strong selection pressure could increase and re-occupy fields to constitute an economic weed burden and impurity in future crops.

Background, Aim and Scope: Transgenic oilseed rape (Brassica napus L.; OSR) is estimated to be environmentally and economically problematic because volunteers and ferals occur frequently and because of its hybridisation potential with several wild and weedy species. A proposed mitigation strategy aims to reduce survival, in particular in conventional OSR crops, by coupling the transgenic target modification with a dwarfing gene to reduce competitive fitness. Our study allowed us to access potential ecological implications of this strategy., Materials and Methods: On a large scale (>500 km(2)), we recorded phenological and population parameters of oilseed rape plants for several years in rural and urban areas of Northern Germany (Bremen and surroundings). The characterising parameter were analysed for differences between wild and cultivated plants., Results: In rural areas, occurrences of feral and volunteer OSR together had an average density of 1.19 populations per square kilometre, in contrast to urban areas where we found 1.68 feral populations per square kilometre on average. Throughout the survey, the vegetation cover at the locations with feral OSR ranged from less than 10% to 100%. Our investigations gave clear empirical evidence that feral OSR was, on average, at least 41% smaller than cultivated OSR, independent of phenological state after onset of flowering., Discussion: The findings can be interpreted as phenotypic adaptation of feral OSR plants. Therefore, it must be asked whether dwarfing could be interpreted as an improvement of pre-adaptation to feral environments. In most of the sites where feral plants occurred, germination and establishment were in locations with disturbed vegetation cover, allowing initial growth without competition. Unless feral establishment of genetically modified dwarfed traits are specifically studied, it would not be safe to assume that the mitigation strategy of dwarfing also reduces dispersal in feral environments., Conclusions and Recommendations: With respect to OSR, we argue that the proposed mitigation approach could increase escape and persistence of transgene varieties rather than reducing them. We conclude that the development of effective hazard mitigation measures in the risk evaluation of genetically modified organisms requires thorough theoretical and empirical ecological analyses rather than assumptions about abstract fitness categories that apply only in parts of the environment where the plant can occur.

Abstract: Most genetically modified (GM) crop plants are designed to be grown on large areas. However, empirical investigations for risk assessment are limited in their temporal and spatial extent. In the case of GM crop plants it is difficult to test the relevance of anticipated risks on the same spatial scale as the intended use. Processes which are difficult to assess experimentally include combinatory effects, interactions between different integration levels, persistence, long distance dispersal and occurrence of rare events. To a limited extent, it is possible to combine results of investigations on small spatial scales in a way that large-scale and long-term implications on the regional scale can be analysed by using modelling and extrapolation approaches. It is thus possible to indicate some of the involved risks which are not accessible otherwise. In this paper we present the results of an extrapolation methodology comprising several scales from the field size up to the landscape level. This methodology aimed at analysing the implications of a large-scale release of genetically modified oilseed rape (GM OSR). The approach consisted of an extrapolation scheme beginning with a landscape analysis which generated representative scenarios considering climate and OSR cultivation characteristics. For the spatial extent of several fields this information was applied in an individual-based model representing ontogeny, dispersal and persistence of cultivated, volunteers and feral oilseed rape. In a final step, simulation results were extrapolated to the region of Northern Germany. Here we focus on the model results which were extrapolated to the regional level by applying a set of ecological indicators which allowed to assess potential implications on this level. These indicators included the number and distribution of flowering GM plants and the dynamics of GM OSR seeds in the soil seedbank. Specific results related to the long-term dynamics in the seedbank and volunteer development. Model results emphasise the long-term consequences of GM OSR cultivation and the explicit necessity to regard high variability in potential GMO admixture. This has to be considered when developing landscape management schemes for co-existence. The extrapolation approach presented here, integrates different traits to assess effects of GMOs on large spatial scales with respect to persistence and dispersal. The developed methodology is equally applicable for other crops, regions and different agricultural conditions. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: We present a mechanistic simulation model (GeneTraMP), which allows analysing the persistence and dispersal of genetically modified plants (GMP) in heterogeneous landscapes. The modelling approach focuses on a spatial explicit representation of both, individual feral and volunteer plants as well as aggregated plant cohorts of crop plants and their reproduction units (pollen and seeds). The plant representation is set up to differentiate several crop species and related wild species. A variable number of genes can be considered, enabling to study a variety of questions related to gene flow, persistence and stacking of genetically modified traits in agricultural and natural landscapes. Different management schemes can be included to quantify and evaluate the outcome of small-scale interactions of natural processes and human activities in relation to various issues of concern in the current debate on GMP concerning environmental impact, biodiversity issues and the coexistence of GM and conventional crops.In this paper we present the general model setup, model structure and the implementation of basic processes concerning spatial configuration, plant development and dispersal of pollen and seeds. As far as possible, these were implemented by applying existing and previously validated model descriptions. The parameterisation was done for oilseed rape (OSR) and the region of northern Germany. Simulation results are presented to provide first insights into the potential of the model to represent and analyse many relevant aspects related to the introduction of genetically modified (GM) OSR crops in agriculture. The model also provides a basis for a spatial extrapolation when being run with appropriate data sets that represent the regional variability of relevant input parameter. [Copyright &y& Elsevier]