Andow, D. A.2; Lövei, Gabor L13; Arpaia, Salvatore3; Wilson, Lewis4; Fontes, Eliana M. G.5; Hilbeck, Angelika6; Lang, Andreas7; Tuat, Nguyên Van8; Pires, C. S. S.9; Sujii, E. R.9; Zwahlen, Claudia2; Birch, A. N. E.10; Capalbo, Deise M. F.11; Prescott, Kristina2; Omoto, Celso12; Zeilinger, Adam R.2
1 Department of Agroecology - Crop Health, Department of Agroecology, Science and Technology, Aarhus University2 Department of Entomology, University of Minnesota3 ENEA-Research Centre Trisaia4 CSIRO Cotton Research Unit5 Embrapa Genetic Resources and Biotechnology Biological Control Area6 Swiss Federal Institute of Technology, Institute of Integrative Biology7 Institute of Environmental Geosciences, University of Basel8 Food Crops Research Institute, MARD9 Embrapa Genetic Genetic Resources and Biotechnology Biological Control Area10 Ecological Science Group, James Hutton Institute11 Embrapa Environment Rodovia SP 34012 Universidade de Sao Paulo-ESALQ13 Department of Agroecology - Crop Health, Department of Agroecology, Science and Technology, Aarhus University
The environmental risks associated with genetically-engineered (GE) organisms have been controversial, and so have the models for the assessment of these risks. We propose an ecologically-based environmental risk assessment (ERA) model that follows the 1998 USEPA guidelines, focusing on potential adverse effects to biological diversity. The approach starts by (1) identifying the local environmental values so the ERA addresses specific concerns associated with local biological diversity. The model simplifies the indicator endpoint selection problem by (2) classifying biological diversity into ecological functional groups and selecting those that deliver the identified environmental values. (3) All of the species or ecosystem processes related to the selected functional groups are identified and (4) multi-criteria decision analysis (MCDA) is used to rank the indicator endpoint entities, which may be species or ecological processes. MCDA focuses on those species and processes that are critical for the identified ecological functions and are likely to be highly exposed to the GE organism. The highest ranked indicator entities are selected for the next step. (5) Relevant risk hypotheses are identified. Knowledge about the specific transgene and its possible environmental effects in other countries can be used to assist development of risk hypotheses. (6) The risk hypotheses are ranked using MCDA with criteria related to the severity of the potential risk. The model emphasizes transparent, expert-driven, ecologically-based decision-making and provides formal methods for completing a screening level-ERA that can focus ERA on the most significant concerns. The process requires substantial human input but the human capital is available in most countries and regions of the world.
Journal of Biosafety, 2013, Vol 22, Issue 3, p. 141-156