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Risk indication of genetically modified organisms (GMO): Modelling environmental exposure and dispersal across different scales
Ecological Indicators 11 (2011) 936–941
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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 Broder Breckling a,*, Hauke Reuter b, Ulrike Middelhoff c, Michael Glemnitz d,**, Angelika Wurbs d, Gunther Schmidt e, Winfried Schro¨der e, Wilhelm Windhorst f a Department of General & Theoretical Ecology, Centre for Environmental Research and Sustainable Technology (UFT), P.O. Box 330440, University of Bremen, 28334 Bremen, Germany b Department of Ecological Modelling, Leibniz Centre of Marine Tropical Ecology (ZMT), Fahrenheitstrasse 6, 28359 Bremen, Germany c Federal Agency for Consumer Protection and Food Safety (BVL), 10117 Berlin, Germany d Leibniz Centre for Agricultural Landscape Research ZALF, Eberswalder Straße 84, 15374 Mu¨ncheberg, Germany e Chair for Landscape Ecology, University of Vechta, P.O. Box 1553, 49364 Vechta, Germany f Ecology Centre, University of Kiel, Christian-Albrechts-Universita¨t Zu Kiel, Ohlshausenstr. 40–80, 24118 Kiel, Germany
A R T I C L E I N F O
A B S T R A C T
Article history: Available online 7 June 2009
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. ß 2009 Elsevier Ltd. All rights reserved.
Keywords: Genetically modified organisms GMO Oilseed rape Brassica napus Up-scaling Risk assessment Modelling
1. Introduction Prediction of large-scale ecological processes is generally difficult and limited because of the complex nature of environ-
* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (B. Breckling), [email protected] (H. Reuter), [email protected] (U. Middelhoff), [email protected] (M. Glemnitz), [email protected] (A. Wurbs), [email protected] (G. Schmidt), [email protected] (W. Schro¨der), [email protected] (W. Windhorst). 1470-160X/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2009.03.002
mental interactions. For the assessment of ecological conditions it is therefore of essential interest to determine contexts which are sufficiently easy to be surveyed. For ecological assessment, sufficiently simple cause–effect networks have to be identified, which provide indicative information about complex interrelations, which are less easy to evaluate (Dale and Beyeler, 2001). In any anticipatory analysis of environmental effects of genetically modified organisms it is intended to understand the functionality of a transgene with regard to physiological as well as to ecological relations. This includes different scales from the molecular to the landscape and regional level (Snow et al., 2005). It is not possible to establish a general framework that would allow
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for a direct extrapolation from the transgene characteristics to large-scale environmental implications, however, a sufficiently complete risk assessment and subsequent post-market monitoring involve statements relevant to the organisation levels from the molecular to the regional level. The special issue ‘‘Risk indication of Genetically Modified Organisms: Oilseed Rape in Northern Germany—A Multi-scale, Model-based Assessment’’, which is presented here, outlines an interdisciplinary approach focusing on aspects that depend on extrapolation of exposition, management and dispersal processes to identify potential large-scale implications from smaller scale information. Though each of the involved steps of the exemplified approach largely uses established scientific methodologies, the targeted combination in the context of genetically modified organisms is innovative in the attempt to facilitate cross-level conclusions. The establishment of indicative relations in the context of genetically modified organisms (GMO) as well as in the general context requires the analysis and understanding how the considered indicator is causally linked to the indicandum (Suter, 2001). In this respect, research on ecological indicators deals with the understanding of ecological networks to find out, how far a simplifying access to the situation of interest is possible. The value of an indicator depends on the two aspects: (1) how well and strict is the relationship of indicans and indicandum in terms of the underlying causal network, and (2) to what extent does the application of the indicator actually simplify access and understanding of the context for which it is indicative? In a general perspective the second aspect involves also the question of representativeness, ecological significance, suitable recording methods, among others. Research in the field of ecological indicators aims to find evidence for the existence of according relations. The suitability of the indicator is then determined by the gain of efficiency to make judgements on the given context (Dale and Beyeler, 2001; Kurtz et al., 2001). Beyond the simple one-to-one relations between indicans and indicandum, the expansion of the concept also implies the option to assess situations involving network relations and relations that cover scaling issues. The latter represents a challenging research frontier (Schneider, 2001; Miller et al., 2004; Urban, 2005). In simple cases, ecological indicators are limited to statements on a specific scale and extent. Working across scales poses additional challenges. This special issue deals with such a context. After a preceding special issue in Ecological Indicators (Mander et al., 2005) discussed general terms, in this series of contributions we apply up-scaling in the context of effect indication of genetically modified plants (GMP) in agriculture. Anticipative indication of environmental risk involved in such an exposure is difficult, in particular because experimental tests are spatially and temporally limited. However, GMP releases and in particular notifications for commercial use can have implications on larger scales where effects cannot be tested in advance (Stone, 1994). Therefore it is an important ecological and economic question to investigate, whether relations accessible on smaller scale allow the analysis and indication of relevant environmental effects on scales which are not directly accessible in experiments. The special issue introduced here, presents an approach together with a case study to estimate large-scale implications of GMP dispersal and environmental exposure on the basis of a synoptic analysis and geo-statistic extrapolation of model results. This allows to discuss the potential of modelling for an indication of effects on larger scales employing a synthesis of known relations on smaller scales. Moreover, the study provides an example for the integration and application of methodological tools from different disciplines such as physiology, vegetation ecology, population biology, agricultural sciences, geography and ecological modelling.
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GMP are developed using laboratory methods that allow to add or alter genetic constituents in a way that is not achievable through conventional breeding. Molecular carriers of genetic information (usually DNA) are taken out of their original context and are integrated into a new physiological environment (Traavik et al., 2007). GMO carry genetic information which has not yet been part of the gene pool of the natural population or cultivars of the respective species. The intention of the genetic alteration is to provide the organism with a new property. Many of the physiological implications of a GMO can be studied in containment. However, responses to the potential combinations of abiotic (climate, soil) conditions, and to the highly variable biotic context as well as implications for large scale interactions cannot be fully exhausted through experiments in containment. Though it is not possible to uncover any unforeseen undesirable effect, it is possible to reduce the remaining gap through model calculations, as shown e.g. by Colbach et al. (2001, 2008), and of scenario-conditions and subsequent geo-statistical up-scaling of the results, as we outline here. 2. Indication in risk analysis of genetically modified plants (GMP) There is a wide variety of risks that has to be considered and – if possible excluded – in the GMP context. The specific importance to anticipate potential risks comes from the fact, that organisms can self-reproduce and that containment of GMP once released for commercial purposes has proven to be nearly impossible (Ellstrand, 2003; Jenczweski et al., 2003; Marvier and Van Acker, 2005). Thus it may be difficult or impossible to correct these decisions in case that unintended implications become apparent. Potential environmental risks are – among others: Horizontal gene transfer: The transgene may be transferred into other species (in particular bacteria) as a rare event, e.g. through incorporation and integration of free DNA (Gebhard and Smalla, 1998; Demaneche et al., 2001; De Vries et al., 2004). Vertical gene transfer: The transgenes may be passed to generations of offspring where they are not intended to persist (e.g. Hall et al., 2000). Persistence: In case transgenic organisms escape cultivation, persisting transgenes may combine with other genetic material (existing in the species’ gene pool) and give rise to untested and unpredictable (combinatory) effects (Pessel et al., 2001). Hybridisation: The transgenes may introgress into the gene pool of related species if the possibility of hybridisation exists. In case of a selective advantage or if genetic drift acts accordingly, the transgene may increase in frequency and give rise to unintended effects in wild species, e.g. invasiveness, or weediness (Beckie et al., 2003; Legere, 2005). Higher order effects: Depending on the type of transgene, there may be effects on food chains in ecosystems that lead to alterations in biodiversity and bioelement cycling (Watkinson et al., 2000). Indirect effects: Alterations in crop management enabled by transgene cultivars, such as a modified pest management, may have relevant implications for agricultural biodiversity as well as for biota on the landscape level (Benbrook, 2004). Moreover, transgene cultivars may show competitive advantages due to less sensitivity to anthropogenic impacts and modify the structure of biocoenoses in arable landscapes (Knispel et al., 2008). Agricultural risks involve in particular cross-contamination of conventional neighbouring crops through pollen transfer and seed or diaspore dispersal (e.g. Messean et al., 2006). Volunteers in other crops may hinder the use of intended crop management measures
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(Van Acker et al., 2004) or require additional efforts for their control. Commercial risks involve cases when conventional crops cannot be sold as GM free and lose their intended market. In Europe, the 0.9% labelling threshold applies to conventional crops, which have to be marketed as genetically modified, if they contain more than 0.9% GM impurities (Regulation EC No 1839/2003). Anticipation of risks involved in the release of GMP uses the results of laboratory and field testing. Any statement on effects of larger scales which are not directly accessible through experimentation can be made either on the basis of qualitative expert statements (Bock et al., 2002; Messean et al., 2006) or on the basis of quantitative extrapolations through up-scaling. Since risk assessment of GMP makes extensive use of ecological knowledge, improved knowledge in this field advances also other contexts, in particular the relation of agricultural applications and biodiversity. The approach presented in this special issue does not cover all of the relevant risks. Not covered are e.g. nutritional and food safety issues, however, issues that involve dispersal and persistence can be well approximated through up-scaling. We demonstrate and discuss a model approach to anticipate and indicate regional dynamics of a particular GMP. By applying a generic model, the approach can be also adapted to other cases. In the given context, indication not only simplifies the access to the intended level of information. In fact, it appeared to be the only way to anticipate effects on larger scales before they actually occur. The applied indicator approach does not work on the level of a simple parameter to be measured and then extrapolated. The approach required to bring together a larger set of information and linking it through model evaluation and scenario building. It was required to combine empirical results, the available information on the regional scale, and integrate it in an overall context. This provided an indicator set, which allowed to trace different kind of direct and indirect effects. The spatial explicitness allowed to identify regions with specific risks. For up-scaling, information on small-scale processes was combined with available regional information on the spatiotemporal distribution of the context variables. Geo-statistics were used to indicate event frequencies which then expanded the range of potential statements. Below, the approach is outlined. The contributions to this special volume go into detail about the contributions from different disciplines. 3. The up-scaling approach The basic idea in up-scaling the information from small-scale field testing to larger geographical regions was a combination of topdown and bottom-up analyses (Fig. 1). The top-down approach contributed a regional analysis of the distribution of relevant parameter values. The bottom-up analysis integrated the known interaction into a small-scale simulation model. Top-down and bottom-up were brought together by preparing parameter input datasets covering the regional variability of relevant parameter combinations, running the model for the conditions and producing a result data bank covering the regional heterogeneity of conditions. From the bottom-up perspective, it was required to understand and model small-scale interactions in full detail. In the given case we used the scale of a square kilometre for detailed process representation involving crop growth (and its weather dependence), crop management, crop rotations as well as (cross)pollination, volunteers and feral growth of GMP outside cultivation. Approaches on this local level made use of empirical investigation results. From field testing and general agricultural knowledge on the particular crops there is reasonable experience to set-up and validate models of different processes on that level (e.g. Habekotte, 1997a,b; Champolivier et al., 1999; Colbach et al., 2001; Rieger et al., 2002). A modelling approach specifically
Fig. 1. The up-scaling approach as it combines large-scale and small-scale information. Bottom-up data input was used to model local dynamics. General knowledge on the specific crop and information from field testing was incorporated on this level. In a top-down approach, the regional variation in driving forces was analysed and used to specify scenario sets to run the model under different input conditions. The up-scaling was then achieved by selecting the model output matching the specific sites that make up the regional context. Since the model contained processes like small-scale dispersal, seed bank development and crosscontamination of fields, regional statements on the relevance of these processes on larger scale could be derived.
adapted for the up-scaling purpose is described by Middelhoff et al. (2011b). In a top-down approach it was required to analyse to which extent the relevant parameters driving the model varied in the wider regional context for which up-scaling was intended. This required the evaluation of various sources of regional data – geographical maps, climate and weather data, agricultural statistics, and remote sensing images (Breckling et al., 2011) as an important source for large-scale high resolution data. These data sources were classified to identify the spatial and temporal distribution of different crops and to also facilitate other regional data in high resolution (Glemnitz et al., 2011). Regionally varying parameters are e.g. landscape structure, field geometries, crop rotations and weather data. After determining the range of parameter variation, an analysis of frequency and combination of occurrence were required. This was a task for geo-statistical classification as described in the contribution of Schmidt and Schro¨der (2011). The regional data analysis then allowed to select those sites in the region which had the highest degree of representativeness with regard to the overall regional variability (geo-statistical selection). Top-down and bottom-up approaches came together when preparing characteristic model input datasets for the selected sites. The selection of sites according to their degree of representing certain parameter combinations in the region allowed to capture a maximum of biogeographic variability with a relative minimum of simulation runs with different input parameter. It turned out, that to a considerable extent, the parameter varied independent of each other. Therefore, a combinatory approach was reasonable. Despite the attempt to reduce the number of relevant model input parameter combinations, the number of required model runs remained relatively large (e.g. different field sizes combined with climate and different crop rotations, see Reuter et al., 2011). The data output of the model runs was then transferred into a result data bank. As a final task for up-scaling, maps of the region were used and simplified in form of a grid. The region was represented by a grid of 5 km 5 km. For each grid element the actual (or interpolated) combination of parameter was assessed. Then the simulation
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output from the result data bank obtained from the run with the parameter combination closest to the one at the grid cell was assigned to that grid element. This way, the entire region was filled with model output. In a first approach, thus, up-scaling was reduced to fill the regional context with simulation results that were closest to the regionally varying parameter combinations. This is a simplification of regional dynamics which is restricted to the autonomous local dynamics. Every grid point was followed separately in time. To simulate mutual impact between neighbouring grid points would require an additional iteration between the local and regional scale. This is currently not implemented in our approach and would require further development, which is a technically feasible option, however. Up-scaling GMP effects required to combine information on different scales – local interactions and parameter variation on the large scale. While the bottom-up information outlines the response space, the top-down approach attempts to delimit the existing biogeographic variability that exists on the regional level. The concept is of general relevance to analyse effects of crop growth on a regional scale. 4. Oilseed rape cultivation in Northern Germany as a case study The case study exemplifying the feasibility of up-scaling GMP effects deals with oilseed rape (Brassica napus). This is a relevant crop world wide in temperate climates and particularly in Europe (FAO, 2005). The potential introduction of GM oilseed rape may have major implications on the agricultural practice in general, like changes in herbicide application frequency and shifts to postemergence applications, which affect weeds in a different way than pre-emergence application. Further on, changes in crop rotation pattern might be an issue. In Germany, oilseed rape is the third most frequently cultivated crop in current land use (DESTATIS, 2001), and it is suitable to be grown on nearly three quarter of all agricultural land. For Europe it is of specific interest, since the centre of biodiversity for related species that could hybridise with GM cultivars is also located in Europe (OECD, 1997). For other major cultivars in Germany, the centre of origin is in overseas regions – e.g. for maize in Mexico, for potato in Peru, and for some grain species in the Middle East. In Europe, oilseed rape plants spontaneously occur with a relatively high frequency not only as volunteers in cultivation areas but also as feral plants along roadsides and on ruderal locations (Theenhaus et al., 2005; Menzel, 2006, Reuter et al., 2008). It builds seed banks in cultivated fields which can give rise to volunteer populations (Schlink, 1998; Lutman et al., 2005). Therefore, the species allows to study a larger extent of relations also relevant in the context of other crops. 5. The contributions to the special issue The contributions to this special issue present the details of the above outlined up-scaling approach from the perspective of the involved disciplinary contexts. First, the results of an analysis of the regional conditions are given. Then, the modelling approach dealing with the small-scale interactions is explained. Subsequently, details of the geo-statistical procedure for up-scaling are contributed. An in-depth study on dispersal frequencies on the level of one entire federal state within Germany completes the papers of the special issue. 6. Characterisation of the regional range of parameter and process variability As regional scale we refer to spatial units going beyond ecosystems and neighbouring ecosystem complexes (landscapes).
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Unlike landscapes, which are usually defined according to functional criteria, regions can encompass larger administrative units like counties or federal states and even national territories. The regional characterisation combines datasets that provide information on relevant environmental conditions that are temporally and spatially distributed. This was required for the scenario-selection, and for the assignment of the model results to the sub-regions where they apply. The data preparation and collation are described in the first three contributions: 6.1. Remote sensing analysis (Breckling et al., 2011) The contribution describes how to use data from satellite images to locate the cultivation sites of oilseed rape on the regional scale. This provided crucial information which facilitated – among others – a frequency analysis of field sizes and neighbourhood relationships, which were e.g. relevant in the context of pollen dispersal. The data were obtained from LANDSAT images of Northern Germany. Since several frames (satellite images) had to be combined for this purpose and the spectral characteristics of oilseed rape are not homogeneous across the region (because of different climate driven development stages) the use of specifically developed identification algorithms was required. 6.2. Climate regionalisation (Schmidt and Schro¨der, 2011) Meteorological data provide an important driving condition for modelling plant and crop development. Since climate data are available through Germany’s National Meteorological Service (DWD), it was possible to regionalise not only average conditions of wind direction and intensity, sunshine, temperature and rainfall, but also the given regional and temporal variation. Spatial interpolation between the observation sites was required. The contribution presents the methodology and the results for the intended case study in Northern Germany. The procedure can be used in analogy for other regions with sufficient meteorological data coverage. 6.3. Crop rotation pattern analysis (Glemnitz et al., 2011) Information about agricultural practice, management, and crop rotation was required on the regional level. Usually, this information is not available in a very high resolution, however, it was approximated from agricultural statistics on the smallest available spatial scale (usually district level, in specific cases also on community level). The authors show an approach, how the relevant measures were deduced from land use statistics combined with agricultural background knowledge and recommended cultivation practices. The methodology took into account changes in land use, as induced by changes in agricultural policy or market conditions. Temporal and spatial distribution of the cultivation structure was provided to be used as an indicator, covering different impacts on the farmers decision-making as it resulted from geographical conditions (e.g. differences in soil quality), farming structure (e.g. distribution of different farm types) and actual market prices. 6.4. Modelling approaches: oilseed rape process modelling (Middelhoff et al., 2011b) The contribution describes the processes considered to simulate small-scale dynamics of oilseed rape population development. This includes a climate dependent sub-model of plant development, pollen transfer, seed set, crop management response as well as volunteer and feral population characteristics and survival. To cope with the relevant processes, a separate regional
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approach was used to simulate large-scale background pollen transfer. 6.5. Geo-statistic up-scaling methodology (Reuter et al., 2011) The model as outlined by Middelhoff et al. (2011b) was run with a range of input datasets that depict the regional variability of the northern German landscapes. Using the modelling results as a basis it is explained, how the range of model input data sets was selected (scenario building) and how the model results were generated to fill the regional range of parameter variation. This executed the central up-scaling step. 6.6. Demonstration of the achievable indicative potential: case study Schleswig–Holstein (Northern Germany) (Middelhoff et al., 2011a) In this contribution, a regional up-scaling was applied for Schleswig–Holstein, the most northern federal state of Germany. Due to additional high-resolution information on farming strategies on the local level, which was not available for the whole territory of Northern Germany, a much finer up-scaling was possible and implications of GM oilseed rape cultivation are discussed in further detail. This contribution illustrates how the presented methods can support decision-making processes if they are incorporated into the planning of an environmental monitoring of commercial GM crops or of necessary co-existence measures. 7. Final remark The overall approach demonstrated, how available information sources and process knowledge on empirically accessible scales was combined to deal with the regional scale. The details that are provided by the contributions of this special issue show, that integrative analyses and risk indication concerning, in particular, pollen transfer and seed dispersal can be scaled up to the regional level. The contributions brought together in this special volume attempted to show, that it was possible to go beyond the directly accessible empirical range of investigation. It has to be emphasised, that the approach required a combination of expertise in different fields, making it a truly interdisciplinary work. Remote sensing, (i.e. environmental physics), geography, meteorology, agricultural science, together with ecology and ecological modelling needed to be involved. It was apparent, that up-scaling in this field cannot be achieved from a single disciplinary perspective. Acknowledgements The authors gratefully acknowledge funding by the German Federal Ministry of Education and Research (BMBF) under grant FKZ 0312637 A, B, C, and D, FKZ 07VPS14A-D and by the European Commission in the 6th Framework Programme, FP6-2002-SSP1 Contract no.: 502981. References Beckie, H.J., Warwick, S.I., Nair, H., Seguin-Swartz, G., 2003. Gene flow in commercial fields of herbicide-resistant canola (Brassica napus). Ecological Applications 13, 1276–1294. Benbrook, C.M., 2004. Genetically Engineered Crops and Pesticide Use in the United States: The First Nine Years. BioTech InfoNet, Technical Paper Number 7. Bock, A.K., Lheureux, K., Libeau, M., Nilsagard, H., Rodriguez-Cerezo, E., 2002. Scenarios for co-existence of genetically modified, conventional and organic crops in European agriculture. IPTS/DG JRC Technical Report. European Commission (EUR 20394 EN) Commissioned by DG Agriculture (Technical Report, 155 p.). Breckling, B., Laue, H., Pehlke, H., 2011. Remote sensing as a data source to analyse regional implications of genetically modified plants in agriculture—oilseed rape (Brassica napus) in Northern Germany. Ecological Indicators 11 (4), 942–950.
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B. Breckling et al. / Ecological Indicators 11 (2011) 936–941 Regulation (EC) No 1830/2003 of the European Parliament and of the Council of 22 September, 2003. Concerning the traceability and labelling of genetically modified organisms and the traceability of food and feed products produced from genetically modified organisms and amending Directive 2001/18/EC, http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2003:268:0024: 0028:EN:PDF (accessed 30.10.2008. Reuter, H., Menzel, G., Pehlke, G., Breckling, B., 2008. Hazard mitigation or mitigation hazard? Would genetically modified dwarfed oilseed rape (Brassica napus L.) increase feral survival?. Environmental Science and Pollution Research 15, 529–535. Reuter, H., Schmidt, G., Schro¨der, W., Middelhoff, U., Pehlke, H., Breckling, B., 2011. Regional distribution of genetically modified organisms (GMO)—up-scaling the dispersal and persistence potential of herbicide resistant oilseed rape (Brassisca napus). Ecological Indicators 11 (4), 989–999. Rieger, M.A., Lamond, M., Preston, C., Powles, S.B., Roush, R.T., 2002. Pollenmediated movement of herbicide resistance between commercial canola fields. Science 296, 2386–2388. ¨ berdauerung von Rapssamen (Brassica napus L.) im Boden. Schlink, S., 1998. 10 Jahre U Mitteilungen der Gesellschaft fu¨r Pflanzenbauwissenschaften 11, 221–222. Schmidt, G., Schro¨der, W., 2011. Regionalisation of climate variability used for modelling the dispersal of genetically modified oil seed rape in Northern Germany. Ecological Indicators 11 (4), 951–963. Schneider, D.C., 2001. The rise of the concept of scale in ecology. BioScience 51, 545–553.
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Snow, A.A., Andow, D.A., Gepts, P., Hallerman, E.M., Power, A., Tiedje, J.M., Wolfenbarger, L.L., 2005. Genetically engineered organisms and the environment: current status and recommendations. Ecological Applications 15, 377–404. Stone, R., 1994. Large plots are next test for transgenic crop safety. Science 266, 1472–1473. Suter II, G.W., 2001. Applicability of indicator monitoring to ecological risk assessment. Ecological Indicators 1, 101–112. Theenhaus, A., Peichl, L., Zeitler, R., Botsch, H.-J., 2005. Monitoring mo¨glicher Auswirkungen von gentechnisch vera¨ndertem herbizidtoleranten Raps auf die einheimische Flora. Report for the German Federal Conservation Agency and the Federal Environmental Agency, FKZ 200 89 412/01. Traavik, T., Nielsen, K.M., Quist, D., 2007. Genetic Engineering of living cells and organisms. In: Traavik, T., Ching, L.L. (Eds.), Biosafety First. Holistic approaches to risk and Uncertainty in Genetic Engineering and Genetically Modified Organisms. Tapir Press, Trondheim, pp. 65–93. Urban, D.L., 2005. Modeling ecological processes across scale. Ecology 86, 1996– 2006. Van Acker, R.C., Brule-Babel, A.L., Friesen, L.F., 2004. Intraspecific gene movement can create environment risk: the example of Roundup Ready wheat in Western Canada. In: Verhoeven, Breckling, B. (Eds.), Risk Hazard Damage, vol. 1. pp. 37– 47. Watkinson, A.R., Freckleton, R.P., Robinson, R.A., Sutherland, W-.J., 2000. Predictions of biodiversity response to genetically modified herbicide-tolerant crops. Science 289, 1554–1557.
Contents lists available at ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
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 Broder Breckling a,*, Hauke Reuter b, Ulrike Middelhoff c, Michael Glemnitz d,**, Angelika Wurbs d, Gunther Schmidt e, Winfried Schro¨der e, Wilhelm Windhorst f a Department of General & Theoretical Ecology, Centre for Environmental Research and Sustainable Technology (UFT), P.O. Box 330440, University of Bremen, 28334 Bremen, Germany b Department of Ecological Modelling, Leibniz Centre of Marine Tropical Ecology (ZMT), Fahrenheitstrasse 6, 28359 Bremen, Germany c Federal Agency for Consumer Protection and Food Safety (BVL), 10117 Berlin, Germany d Leibniz Centre for Agricultural Landscape Research ZALF, Eberswalder Straße 84, 15374 Mu¨ncheberg, Germany e Chair for Landscape Ecology, University of Vechta, P.O. Box 1553, 49364 Vechta, Germany f Ecology Centre, University of Kiel, Christian-Albrechts-Universita¨t Zu Kiel, Ohlshausenstr. 40–80, 24118 Kiel, Germany
A R T I C L E I N F O
A B S T R A C T
Article history: Available online 7 June 2009
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. ß 2009 Elsevier Ltd. All rights reserved.
Keywords: Genetically modified organisms GMO Oilseed rape Brassica napus Up-scaling Risk assessment Modelling
1. Introduction Prediction of large-scale ecological processes is generally difficult and limited because of the complex nature of environ-
* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (B. Breckling), [email protected] (H. Reuter), [email protected] (U. Middelhoff), [email protected] (M. Glemnitz), [email protected] (A. Wurbs), [email protected] (G. Schmidt), [email protected] (W. Schro¨der), [email protected] (W. Windhorst). 1470-160X/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2009.03.002
mental interactions. For the assessment of ecological conditions it is therefore of essential interest to determine contexts which are sufficiently easy to be surveyed. For ecological assessment, sufficiently simple cause–effect networks have to be identified, which provide indicative information about complex interrelations, which are less easy to evaluate (Dale and Beyeler, 2001). In any anticipatory analysis of environmental effects of genetically modified organisms it is intended to understand the functionality of a transgene with regard to physiological as well as to ecological relations. This includes different scales from the molecular to the landscape and regional level (Snow et al., 2005). It is not possible to establish a general framework that would allow
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for a direct extrapolation from the transgene characteristics to large-scale environmental implications, however, a sufficiently complete risk assessment and subsequent post-market monitoring involve statements relevant to the organisation levels from the molecular to the regional level. The special issue ‘‘Risk indication of Genetically Modified Organisms: Oilseed Rape in Northern Germany—A Multi-scale, Model-based Assessment’’, which is presented here, outlines an interdisciplinary approach focusing on aspects that depend on extrapolation of exposition, management and dispersal processes to identify potential large-scale implications from smaller scale information. Though each of the involved steps of the exemplified approach largely uses established scientific methodologies, the targeted combination in the context of genetically modified organisms is innovative in the attempt to facilitate cross-level conclusions. The establishment of indicative relations in the context of genetically modified organisms (GMO) as well as in the general context requires the analysis and understanding how the considered indicator is causally linked to the indicandum (Suter, 2001). In this respect, research on ecological indicators deals with the understanding of ecological networks to find out, how far a simplifying access to the situation of interest is possible. The value of an indicator depends on the two aspects: (1) how well and strict is the relationship of indicans and indicandum in terms of the underlying causal network, and (2) to what extent does the application of the indicator actually simplify access and understanding of the context for which it is indicative? In a general perspective the second aspect involves also the question of representativeness, ecological significance, suitable recording methods, among others. Research in the field of ecological indicators aims to find evidence for the existence of according relations. The suitability of the indicator is then determined by the gain of efficiency to make judgements on the given context (Dale and Beyeler, 2001; Kurtz et al., 2001). Beyond the simple one-to-one relations between indicans and indicandum, the expansion of the concept also implies the option to assess situations involving network relations and relations that cover scaling issues. The latter represents a challenging research frontier (Schneider, 2001; Miller et al., 2004; Urban, 2005). In simple cases, ecological indicators are limited to statements on a specific scale and extent. Working across scales poses additional challenges. This special issue deals with such a context. After a preceding special issue in Ecological Indicators (Mander et al., 2005) discussed general terms, in this series of contributions we apply up-scaling in the context of effect indication of genetically modified plants (GMP) in agriculture. Anticipative indication of environmental risk involved in such an exposure is difficult, in particular because experimental tests are spatially and temporally limited. However, GMP releases and in particular notifications for commercial use can have implications on larger scales where effects cannot be tested in advance (Stone, 1994). Therefore it is an important ecological and economic question to investigate, whether relations accessible on smaller scale allow the analysis and indication of relevant environmental effects on scales which are not directly accessible in experiments. The special issue introduced here, presents an approach together with a case study to estimate large-scale implications of GMP dispersal and environmental exposure on the basis of a synoptic analysis and geo-statistic extrapolation of model results. This allows to discuss the potential of modelling for an indication of effects on larger scales employing a synthesis of known relations on smaller scales. Moreover, the study provides an example for the integration and application of methodological tools from different disciplines such as physiology, vegetation ecology, population biology, agricultural sciences, geography and ecological modelling.
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GMP are developed using laboratory methods that allow to add or alter genetic constituents in a way that is not achievable through conventional breeding. Molecular carriers of genetic information (usually DNA) are taken out of their original context and are integrated into a new physiological environment (Traavik et al., 2007). GMO carry genetic information which has not yet been part of the gene pool of the natural population or cultivars of the respective species. The intention of the genetic alteration is to provide the organism with a new property. Many of the physiological implications of a GMO can be studied in containment. However, responses to the potential combinations of abiotic (climate, soil) conditions, and to the highly variable biotic context as well as implications for large scale interactions cannot be fully exhausted through experiments in containment. Though it is not possible to uncover any unforeseen undesirable effect, it is possible to reduce the remaining gap through model calculations, as shown e.g. by Colbach et al. (2001, 2008), and of scenario-conditions and subsequent geo-statistical up-scaling of the results, as we outline here. 2. Indication in risk analysis of genetically modified plants (GMP) There is a wide variety of risks that has to be considered and – if possible excluded – in the GMP context. The specific importance to anticipate potential risks comes from the fact, that organisms can self-reproduce and that containment of GMP once released for commercial purposes has proven to be nearly impossible (Ellstrand, 2003; Jenczweski et al., 2003; Marvier and Van Acker, 2005). Thus it may be difficult or impossible to correct these decisions in case that unintended implications become apparent. Potential environmental risks are – among others: Horizontal gene transfer: The transgene may be transferred into other species (in particular bacteria) as a rare event, e.g. through incorporation and integration of free DNA (Gebhard and Smalla, 1998; Demaneche et al., 2001; De Vries et al., 2004). Vertical gene transfer: The transgenes may be passed to generations of offspring where they are not intended to persist (e.g. Hall et al., 2000). Persistence: In case transgenic organisms escape cultivation, persisting transgenes may combine with other genetic material (existing in the species’ gene pool) and give rise to untested and unpredictable (combinatory) effects (Pessel et al., 2001). Hybridisation: The transgenes may introgress into the gene pool of related species if the possibility of hybridisation exists. In case of a selective advantage or if genetic drift acts accordingly, the transgene may increase in frequency and give rise to unintended effects in wild species, e.g. invasiveness, or weediness (Beckie et al., 2003; Legere, 2005). Higher order effects: Depending on the type of transgene, there may be effects on food chains in ecosystems that lead to alterations in biodiversity and bioelement cycling (Watkinson et al., 2000). Indirect effects: Alterations in crop management enabled by transgene cultivars, such as a modified pest management, may have relevant implications for agricultural biodiversity as well as for biota on the landscape level (Benbrook, 2004). Moreover, transgene cultivars may show competitive advantages due to less sensitivity to anthropogenic impacts and modify the structure of biocoenoses in arable landscapes (Knispel et al., 2008). Agricultural risks involve in particular cross-contamination of conventional neighbouring crops through pollen transfer and seed or diaspore dispersal (e.g. Messean et al., 2006). Volunteers in other crops may hinder the use of intended crop management measures
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(Van Acker et al., 2004) or require additional efforts for their control. Commercial risks involve cases when conventional crops cannot be sold as GM free and lose their intended market. In Europe, the 0.9% labelling threshold applies to conventional crops, which have to be marketed as genetically modified, if they contain more than 0.9% GM impurities (Regulation EC No 1839/2003). Anticipation of risks involved in the release of GMP uses the results of laboratory and field testing. Any statement on effects of larger scales which are not directly accessible through experimentation can be made either on the basis of qualitative expert statements (Bock et al., 2002; Messean et al., 2006) or on the basis of quantitative extrapolations through up-scaling. Since risk assessment of GMP makes extensive use of ecological knowledge, improved knowledge in this field advances also other contexts, in particular the relation of agricultural applications and biodiversity. The approach presented in this special issue does not cover all of the relevant risks. Not covered are e.g. nutritional and food safety issues, however, issues that involve dispersal and persistence can be well approximated through up-scaling. We demonstrate and discuss a model approach to anticipate and indicate regional dynamics of a particular GMP. By applying a generic model, the approach can be also adapted to other cases. In the given context, indication not only simplifies the access to the intended level of information. In fact, it appeared to be the only way to anticipate effects on larger scales before they actually occur. The applied indicator approach does not work on the level of a simple parameter to be measured and then extrapolated. The approach required to bring together a larger set of information and linking it through model evaluation and scenario building. It was required to combine empirical results, the available information on the regional scale, and integrate it in an overall context. This provided an indicator set, which allowed to trace different kind of direct and indirect effects. The spatial explicitness allowed to identify regions with specific risks. For up-scaling, information on small-scale processes was combined with available regional information on the spatiotemporal distribution of the context variables. Geo-statistics were used to indicate event frequencies which then expanded the range of potential statements. Below, the approach is outlined. The contributions to this special volume go into detail about the contributions from different disciplines. 3. The up-scaling approach The basic idea in up-scaling the information from small-scale field testing to larger geographical regions was a combination of topdown and bottom-up analyses (Fig. 1). The top-down approach contributed a regional analysis of the distribution of relevant parameter values. The bottom-up analysis integrated the known interaction into a small-scale simulation model. Top-down and bottom-up were brought together by preparing parameter input datasets covering the regional variability of relevant parameter combinations, running the model for the conditions and producing a result data bank covering the regional heterogeneity of conditions. From the bottom-up perspective, it was required to understand and model small-scale interactions in full detail. In the given case we used the scale of a square kilometre for detailed process representation involving crop growth (and its weather dependence), crop management, crop rotations as well as (cross)pollination, volunteers and feral growth of GMP outside cultivation. Approaches on this local level made use of empirical investigation results. From field testing and general agricultural knowledge on the particular crops there is reasonable experience to set-up and validate models of different processes on that level (e.g. Habekotte, 1997a,b; Champolivier et al., 1999; Colbach et al., 2001; Rieger et al., 2002). A modelling approach specifically
Fig. 1. The up-scaling approach as it combines large-scale and small-scale information. Bottom-up data input was used to model local dynamics. General knowledge on the specific crop and information from field testing was incorporated on this level. In a top-down approach, the regional variation in driving forces was analysed and used to specify scenario sets to run the model under different input conditions. The up-scaling was then achieved by selecting the model output matching the specific sites that make up the regional context. Since the model contained processes like small-scale dispersal, seed bank development and crosscontamination of fields, regional statements on the relevance of these processes on larger scale could be derived.
adapted for the up-scaling purpose is described by Middelhoff et al. (2011b). In a top-down approach it was required to analyse to which extent the relevant parameters driving the model varied in the wider regional context for which up-scaling was intended. This required the evaluation of various sources of regional data – geographical maps, climate and weather data, agricultural statistics, and remote sensing images (Breckling et al., 2011) as an important source for large-scale high resolution data. These data sources were classified to identify the spatial and temporal distribution of different crops and to also facilitate other regional data in high resolution (Glemnitz et al., 2011). Regionally varying parameters are e.g. landscape structure, field geometries, crop rotations and weather data. After determining the range of parameter variation, an analysis of frequency and combination of occurrence were required. This was a task for geo-statistical classification as described in the contribution of Schmidt and Schro¨der (2011). The regional data analysis then allowed to select those sites in the region which had the highest degree of representativeness with regard to the overall regional variability (geo-statistical selection). Top-down and bottom-up approaches came together when preparing characteristic model input datasets for the selected sites. The selection of sites according to their degree of representing certain parameter combinations in the region allowed to capture a maximum of biogeographic variability with a relative minimum of simulation runs with different input parameter. It turned out, that to a considerable extent, the parameter varied independent of each other. Therefore, a combinatory approach was reasonable. Despite the attempt to reduce the number of relevant model input parameter combinations, the number of required model runs remained relatively large (e.g. different field sizes combined with climate and different crop rotations, see Reuter et al., 2011). The data output of the model runs was then transferred into a result data bank. As a final task for up-scaling, maps of the region were used and simplified in form of a grid. The region was represented by a grid of 5 km 5 km. For each grid element the actual (or interpolated) combination of parameter was assessed. Then the simulation
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output from the result data bank obtained from the run with the parameter combination closest to the one at the grid cell was assigned to that grid element. This way, the entire region was filled with model output. In a first approach, thus, up-scaling was reduced to fill the regional context with simulation results that were closest to the regionally varying parameter combinations. This is a simplification of regional dynamics which is restricted to the autonomous local dynamics. Every grid point was followed separately in time. To simulate mutual impact between neighbouring grid points would require an additional iteration between the local and regional scale. This is currently not implemented in our approach and would require further development, which is a technically feasible option, however. Up-scaling GMP effects required to combine information on different scales – local interactions and parameter variation on the large scale. While the bottom-up information outlines the response space, the top-down approach attempts to delimit the existing biogeographic variability that exists on the regional level. The concept is of general relevance to analyse effects of crop growth on a regional scale. 4. Oilseed rape cultivation in Northern Germany as a case study The case study exemplifying the feasibility of up-scaling GMP effects deals with oilseed rape (Brassica napus). This is a relevant crop world wide in temperate climates and particularly in Europe (FAO, 2005). The potential introduction of GM oilseed rape may have major implications on the agricultural practice in general, like changes in herbicide application frequency and shifts to postemergence applications, which affect weeds in a different way than pre-emergence application. Further on, changes in crop rotation pattern might be an issue. In Germany, oilseed rape is the third most frequently cultivated crop in current land use (DESTATIS, 2001), and it is suitable to be grown on nearly three quarter of all agricultural land. For Europe it is of specific interest, since the centre of biodiversity for related species that could hybridise with GM cultivars is also located in Europe (OECD, 1997). For other major cultivars in Germany, the centre of origin is in overseas regions – e.g. for maize in Mexico, for potato in Peru, and for some grain species in the Middle East. In Europe, oilseed rape plants spontaneously occur with a relatively high frequency not only as volunteers in cultivation areas but also as feral plants along roadsides and on ruderal locations (Theenhaus et al., 2005; Menzel, 2006, Reuter et al., 2008). It builds seed banks in cultivated fields which can give rise to volunteer populations (Schlink, 1998; Lutman et al., 2005). Therefore, the species allows to study a larger extent of relations also relevant in the context of other crops. 5. The contributions to the special issue The contributions to this special issue present the details of the above outlined up-scaling approach from the perspective of the involved disciplinary contexts. First, the results of an analysis of the regional conditions are given. Then, the modelling approach dealing with the small-scale interactions is explained. Subsequently, details of the geo-statistical procedure for up-scaling are contributed. An in-depth study on dispersal frequencies on the level of one entire federal state within Germany completes the papers of the special issue. 6. Characterisation of the regional range of parameter and process variability As regional scale we refer to spatial units going beyond ecosystems and neighbouring ecosystem complexes (landscapes).
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Unlike landscapes, which are usually defined according to functional criteria, regions can encompass larger administrative units like counties or federal states and even national territories. The regional characterisation combines datasets that provide information on relevant environmental conditions that are temporally and spatially distributed. This was required for the scenario-selection, and for the assignment of the model results to the sub-regions where they apply. The data preparation and collation are described in the first three contributions: 6.1. Remote sensing analysis (Breckling et al., 2011) The contribution describes how to use data from satellite images to locate the cultivation sites of oilseed rape on the regional scale. This provided crucial information which facilitated – among others – a frequency analysis of field sizes and neighbourhood relationships, which were e.g. relevant in the context of pollen dispersal. The data were obtained from LANDSAT images of Northern Germany. Since several frames (satellite images) had to be combined for this purpose and the spectral characteristics of oilseed rape are not homogeneous across the region (because of different climate driven development stages) the use of specifically developed identification algorithms was required. 6.2. Climate regionalisation (Schmidt and Schro¨der, 2011) Meteorological data provide an important driving condition for modelling plant and crop development. Since climate data are available through Germany’s National Meteorological Service (DWD), it was possible to regionalise not only average conditions of wind direction and intensity, sunshine, temperature and rainfall, but also the given regional and temporal variation. Spatial interpolation between the observation sites was required. The contribution presents the methodology and the results for the intended case study in Northern Germany. The procedure can be used in analogy for other regions with sufficient meteorological data coverage. 6.3. Crop rotation pattern analysis (Glemnitz et al., 2011) Information about agricultural practice, management, and crop rotation was required on the regional level. Usually, this information is not available in a very high resolution, however, it was approximated from agricultural statistics on the smallest available spatial scale (usually district level, in specific cases also on community level). The authors show an approach, how the relevant measures were deduced from land use statistics combined with agricultural background knowledge and recommended cultivation practices. The methodology took into account changes in land use, as induced by changes in agricultural policy or market conditions. Temporal and spatial distribution of the cultivation structure was provided to be used as an indicator, covering different impacts on the farmers decision-making as it resulted from geographical conditions (e.g. differences in soil quality), farming structure (e.g. distribution of different farm types) and actual market prices. 6.4. Modelling approaches: oilseed rape process modelling (Middelhoff et al., 2011b) The contribution describes the processes considered to simulate small-scale dynamics of oilseed rape population development. This includes a climate dependent sub-model of plant development, pollen transfer, seed set, crop management response as well as volunteer and feral population characteristics and survival. To cope with the relevant processes, a separate regional
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approach was used to simulate large-scale background pollen transfer. 6.5. Geo-statistic up-scaling methodology (Reuter et al., 2011) The model as outlined by Middelhoff et al. (2011b) was run with a range of input datasets that depict the regional variability of the northern German landscapes. Using the modelling results as a basis it is explained, how the range of model input data sets was selected (scenario building) and how the model results were generated to fill the regional range of parameter variation. This executed the central up-scaling step. 6.6. Demonstration of the achievable indicative potential: case study Schleswig–Holstein (Northern Germany) (Middelhoff et al., 2011a) In this contribution, a regional up-scaling was applied for Schleswig–Holstein, the most northern federal state of Germany. Due to additional high-resolution information on farming strategies on the local level, which was not available for the whole territory of Northern Germany, a much finer up-scaling was possible and implications of GM oilseed rape cultivation are discussed in further detail. This contribution illustrates how the presented methods can support decision-making processes if they are incorporated into the planning of an environmental monitoring of commercial GM crops or of necessary co-existence measures. 7. Final remark The overall approach demonstrated, how available information sources and process knowledge on empirically accessible scales was combined to deal with the regional scale. The details that are provided by the contributions of this special issue show, that integrative analyses and risk indication concerning, in particular, pollen transfer and seed dispersal can be scaled up to the regional level. The contributions brought together in this special volume attempted to show, that it was possible to go beyond the directly accessible empirical range of investigation. It has to be emphasised, that the approach required a combination of expertise in different fields, making it a truly interdisciplinary work. Remote sensing, (i.e. environmental physics), geography, meteorology, agricultural science, together with ecology and ecological modelling needed to be involved. It was apparent, that up-scaling in this field cannot be achieved from a single disciplinary perspective. Acknowledgements The authors gratefully acknowledge funding by the German Federal Ministry of Education and Research (BMBF) under grant FKZ 0312637 A, B, C, and D, FKZ 07VPS14A-D and by the European Commission in the 6th Framework Programme, FP6-2002-SSP1 Contract no.: 502981. References Beckie, H.J., Warwick, S.I., Nair, H., Seguin-Swartz, G., 2003. Gene flow in commercial fields of herbicide-resistant canola (Brassica napus). Ecological Applications 13, 1276–1294. Benbrook, C.M., 2004. Genetically Engineered Crops and Pesticide Use in the United States: The First Nine Years. BioTech InfoNet, Technical Paper Number 7. Bock, A.K., Lheureux, K., Libeau, M., Nilsagard, H., Rodriguez-Cerezo, E., 2002. Scenarios for co-existence of genetically modified, conventional and organic crops in European agriculture. IPTS/DG JRC Technical Report. European Commission (EUR 20394 EN) Commissioned by DG Agriculture (Technical Report, 155 p.). Breckling, B., Laue, H., Pehlke, H., 2011. Remote sensing as a data source to analyse regional implications of genetically modified plants in agriculture—oilseed rape (Brassica napus) in Northern Germany. Ecological Indicators 11 (4), 942–950.
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