Landscape Ecology

, Volume 23, Issue 9, pp 1067–1079 | Cite as

Spatial sensitivity of maize gene-flow to landscape pattern: a simulation approach

  • Valérie Viaud
  • Hervé Monod
  • Claire Lavigne
  • Frédérique Angevin
  • Katarzyna Adamczyk
Research Article


Pollen dispersal is a critical process defining connectivity among plant populations. In the context of genetically modified (GM) crops in conventional agricultural systems, strategies based on spatial separation are promoted to reduce functional connectivity between GM and non-GM crop fields. Field experiments as well as simulation studies have stressed the dependence of maize gene flow on distances between source and receptor fields and on their spatial configuration. However, the influence of whole landscape patterns is still poorly understood. Spatially explicit models, such as MAPOD-maize, are thus useful tools to address this question. In this paper we developed a methodological approach to investigate the sensitivity of cross-pollination rates among GM and non-GM maize in a landscape simulated with MAPOD-maize. The influence of landscape pattern on model output was studied at the landscape and field scales, including interactions with other model inputs such as cultivar characteristics and wind conditions. At the landscape scale, maize configuration (proportion of and spatial arrangement in a given field pattern) was shown to be an important factor influencing cross-pollination rate between GM and non-GM maize whereas the effect of the field pattern itself was lower. At the field scale, distance to the nearest GM maize field was confirmed as a predominant factor explaining cross-pollination rate. The metrics describing the pattern of GM maize in the area surrounding selected non-GM maize fields appeared as pertinent complementary variables. In contrast, field geometry and field pattern resulted in little additional information at this scale.


Cross-pollination Global sensitivity analysis Landscape metrics Multi-scale analysis Pollen dispersal 



We thank the Institute for the Protection and Security of the Citizen (Joint Research Centre of the European Union) and AUP-ONIGC for providing the maps of actual landscapes. We are very grateful to Mathieu Leclaire for his assistance with using MAPOD and to Mickael Corson for English corrections. This work was supported by the research program “Impact des OGM” (2002–2006) funded by the French Ministry of Research and by the European project SIGMEA (Contract no. 502981).

Supplementary material

10980_2008_9264_MOESM1_ESM.doc (2.8 mb)
(DOC 2833 kb)


  1. Angevin F, Klein EK, Choimet C, Gauffreteau A, Lavigne C, Messéan A, Meynard JM (2008) Modelling impacts of cropping systems and climate on maize cross-pollination in agricultural landscapes: the MAPOD model. Eur J Agron 28:471–484CrossRefGoogle Scholar
  2. Bateman AJ (1947a) Contamination in seed crops II: wind pollination. Heredity 1:235–246CrossRefGoogle Scholar
  3. Bateman AJ (1947b) Contamination in seed crops. III. relation with isolation distance. Heredity 1:303–336CrossRefGoogle Scholar
  4. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  5. Breiman L, Cutler A (2003) Random forests manual v4.0. Technical report, UC Berkeley. Available online at
  6. Campolongo F, Saltelli A (2000) Design of experiments. In: Saltelli A, Chan K, Scott M (eds) Sensitivity analysis. Wiley, New-York, pp 51–63Google Scholar
  7. Ceddia MG, Bartlett M, Perrings C (2007) Landscape gene flow, coexistence and threshold effect: the case of genetically modified herbicide tolerant oilseed rape (Brassica napus). Ecol Model 205:169–180CrossRefGoogle Scholar
  8. Colbach N, Molinari N, Meynard JM, Messéan A (2005) Spatial aspects of gene flow between rapeseed varieties and volunteers. Agron Sustain Dev 25:355–368CrossRefGoogle Scholar
  9. Crosetto M, Tarantola S, Saltelli A (2000) Sensitivity and uncertainty analysis in spatial modelling based on GIS. Agr Ecosyst Environ 81:71–79CrossRefGoogle Scholar
  10. Damgaard C, Kjellsson G (2005) Gene flow of oilseed rape (Brassica napus) according to isolation distance and buffer zone. Agr Ecosyst Environ 108:291–301CrossRefGoogle Scholar
  11. De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178–3192CrossRefGoogle Scholar
  12. Delgado MG, Sendra JB (2004) Sensitivity analysis in multicriteria spatial decision-making: a review. Hum Ecol Risk Assess 10:1173–1187CrossRefGoogle Scholar
  13. Hoyle M, Cresswell JE (2007) The effect of wind direction on cross-pollination in wind-pollinated GM crops. Ecol Appl 17:1234–1243PubMedCrossRefGoogle Scholar
  14. Jager HI, Ashwood TL, Jackson BL, King AW (2005) Spatial uncertainty analysis of population models. Ecol Model 185:13–27CrossRefGoogle Scholar
  15. Klein EK, Lavigne C, Foueillassar X, Gouyon PH, Laredo C (2003) Corn pollen dispersal: quasi-mechanistic models and field experiments. Ecol Monogr 73:131–150CrossRefGoogle Scholar
  16. Klein EK, Lavigne C, Picault H, Michel R, Gouyon PH (2006) Pollen dispersal of oilseed rape: estimation of the dispersal function and effects of field dimension. J Appl Ecol 43:141–151CrossRefGoogle Scholar
  17. Kuparinen A, Schurr F, Tackenberg O, O’Hara RB (2007) Air-mediated pollen flow from genetically modified to conventional crops. Ecol Appl 17:431–440PubMedCrossRefGoogle Scholar
  18. Lavigne C, Klein EK, Mari JF, Le Ber F, Adamczyk K, Monod H, Angevin F (2008) How do genetically modified (GM) crops contribute to background levels of GM pollen in an agricultural landscape? J Appl Ecol 45:1104–1113CrossRefGoogle Scholar
  19. Makowski D (2005) Comparison of risk indicators for Sclerotinia control in oilseed rape. Crop Prot 24:527–531CrossRefGoogle Scholar
  20. Messéan A, Angevin F, Gomez-Barbero M, Menrad K, Rodriguez-Cerezo E (2006) New case studies on the coexistence of GM and non-GM crops in European agriculture. Technical Report EUR 22102 ENGoogle Scholar
  21. Monod H, Naud C, Makowski D (2006) Uncertainty and sensitivity analysis for crop models. In: Wallach D, Makowski D, Jones JW (eds) Working with dynamic crop models. Elsevier, Amsterdam, pp 55–99Google Scholar
  22. Paterniani E, Stort AC (1974) Effective maize pollen dispersal in the field. Euphytica 23:129–134CrossRefGoogle Scholar
  23. Raynor GS, Ogden EC, Hayes JV (1972) Dispersion and deposition of corn pollen from experimental sources. Agron J 64:420–427Google Scholar
  24. Romary T (2005) Impact de la structure d’un paysage sur les flux de gènes entre parcelles, approche par des méthodes d’analyse de sensibilité de modèle. Master Recherche, Mathématiques option Statistique, Université de Rennes 1Google Scholar
  25. Saltelli A, Tarantola S, Campolongo F (2000) Sensitivity analysis as an ingredient of modeling. Stat Sci 15:377–395CrossRefGoogle Scholar
  26. Sork VL, Nason J, Campbell DR, Fernandez JF (1999) Landscape approaches to the study of gene flow in plants. Trends Ecol Evol 142:219–224CrossRefGoogle Scholar
  27. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293PubMedCrossRefGoogle Scholar
  28. Venables WN, Ripley BD (eds) (2002) Modern applied statistics with S. Springer, New-YorkGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Valérie Viaud
    • 1
  • Hervé Monod
    • 2
  • Claire Lavigne
    • 3
  • Frédérique Angevin
    • 4
  • Katarzyna Adamczyk
    • 2
  1. 1.INRA, Agrocampus Ouest, UMR1069 Soil Agro and hydroSystemRennesFrance
  2. 2.INRA, UR341, MIA-JouyJouy-en-JosasFrance
  3. 3.INRA, UR1115, PSHAvignonFrance
  4. 4.INRA, UAR1240, Eco-InnovThiverval-GrignonFrance

Personalised recommendations