WP4 Design of Decision-Support Tools for coexistence in practice

1. PRactical Implementation of Coexistence in Europe WP4 Design of Decision-Support Tools for coexistence in practice DSS for flexible and cost-effective co-existence measures SDP – 14 November 2012

2. Developing a decision-tool software for providing information about possible coexistence problems and possible solutions atfield and regionallevel. • Specific objectives: • Developing a prototype of a practical and dynamicdecision-support tool (DST) thatwillhelp securecomplianceof non GMO cropswithgiventhresholdsundervariousEuropeancroppingsystemsthroughlocally-adaptedmeasures. • Identification of optimal samplingstrategiesfor tracingatfarm and regionallevel to help stakeholders in theirdecisions.

3. Furtherelaborate on the outcomes of the sigmea EU project

4. SIGMEA gene flow datasets More than 100 datasets collated; Many data on pollen- and seed-mediated gene flow in oilseed rape and maize are available, less on beets, wheat and rice; Landscape-scale demography of crops and pollen-mediated gene flow over medium and long distance to be further investigated; (Messean et al., 2009)

5. Uniform coexistence measures are not optimal Maize cross-pollination datasets Source: Riesgoet al., 2010

6. Adaptation to local conditions is necessary to meet the proportionality principle gene flow models can help

7. Field pattern Sowing date and density Maize varieties Climate MAPOD For each non – GM plant in each field: number of grains with the transgene Proportion of GM grains in non OGM harvest (Angevin et al., 2008)

8. OGM > 2.25% [0.9% ; 2.25%[ [0.6% ; 0.9%[ [0.4% ; 0.6%[ [0.1% ; 0.4%[ [0.01% ; 0.1%[ [0 ; 0.01%[ Maize scenario analysis (MAPOD) (What if? How to?) Landscape Pattern Crop management Weather  Required input variables not always known (e.g., before flowering) or available (climate, management practices)  Variability in outcomes not explicitly expressed Genotypes Cropping systems GMO uptake Sowing dates

9. MAPOD Domaine de calcul de Aquilon z lb vent Point source OGM obstacle Source non-OGM Source non-OGM h hb zf (hauteur fleurs femelles) x 0 xs xb L Overall approach • Physical and biologicalmodels Gene flow datasets Adhoc experiments Decision tools Bayesian meta-analysis Fit-for-purpose DSS + variability of AP

10. Three major steps forward • Associate confidence intervals to AP predictions by taking into consideration uncertainty (model and experimental errors) • Bayesian meta-analysis • Design cost-effective and operational tools for end-users • Operational GIS-based decision-support tool • Develop cost-effective sampling strategies • Use of predictive models to optimize sampling

12. Three major steps forward • Associateconfidence intervals to AP predictions by takingintoconsiderationuncertainty (model and experimentalerrors) • Bayesianmeta-analysis • Design cost-effective and operationaltools for end-users • Operational GIS-baseddecision-support tool • Developcost-effective samplingstrategies • Use of predictivemodels to optimizesampling

13. Bayesian meta-analysis • Perform probabilistic predictions of adventitious presence that account for uncertainty and errors in models and datasets; • Adapt the prediction of AP to the available information (the more descriptors available, the more precise the estimation); • When only distances between GM and non-GM fields are available, single distance-based gene flow models • When climatic data are available (wind effect and/or temperature), models considering both distances and wind distribution as parameters will be identified; • When crop management practices are known, more complex models (MAPOD-like). • Allow easy re-estimation of the model parameters as new datasets come in (from experiments and/or monitoring);

14. Field-to-field: only distance available Yi : GM adventitious presence in a sampling point at a distance di from the GM source (in proportion of grains) Y= Dist (AP, Nsample) AP=K e-a.d a ~ Dist(µa, σa) K ~ Dist(µK, σK )

15. Distribution a posteriori of model parameters (Based on five SIGMEA datasets)

16. Distribution of GM adventitious presence in every non-GM field of the landscape • The more factors available, the more precise the decision Field C Field A Field B

17. Workplan: results so far • New datasets included in the existing geneflow database with new datasets (maize) • Still room for additional datasets • Decision situations • Algorithm for Bayesian estimation of parameters (field to field); • Extension to the multi-source/multi-recipient case • Use of different models/datasets

18. Decision-Making Scenarios

19. Optimal landscape design

20. Three major steps forward • Associate confidence intervals to AP predictions by taking into consideration uncertainty (model and experimental errors) • Bayesian meta-analysis • Design cost-effective and operational tools for end-users • Operational GIS-based decision-support tool • Develop cost-effective sampling strategies • Use of predictive models to optimize sampling

21. Workplan • Interface with National registers (BVL) • Ongoing • Specifications of the GIS-based DST (starting) • Interactions with stakeholders needed

22. National register database (BVL) • Objective: to develop free software for the establishment of a GMO location register in line with Art. 31 (3) of Directive 2001/18/EC • Methods: questionnaire to explore the individual needs and national regulations of the Member States • Results: response from 24 of 31 CAs (27 EU-MS plus Croatia, Iceland, Norway and Switzerland) • GMO data collection in the countries: • differentiation between deliberate releases (Part B) and cultivation (Part C) • differences between the countries e.g.: • GPS data (collected only by 5 countries) • organisation of the cadastral data

23. RESULTS Demands of the Member States on a location register

24. RESULTS Additional benefits of a (harmonized) location register

25. CONCLUSIONS • Conclusions: • differences in demands and data organisation in the European countries will have to be considered in the development of a common location register; • BVL will develop a modular system • provides a very flexible input, but delivers a comparable output • open source software • the location register is free of charge

26. Design of the DST First yearachievements • General specifications of the GIS-based coexistence decision support tool; • A draft “user scenarios” document has been written • General concept of application • Main required functionalities • Interactions with AP models • Additional actions • Landscape database research in France and Germany • Layout of interactions with National public register •  Now needs to be refined with potential users

27. General DST workflow

28. Three major steps forward • Associate confidence intervals to AP predictions by taking into consideration uncertainty (model and experimental errors) • Bayesian meta-analysis • Design cost-effective and operational tools for end-users • Operational GIS-based decision-support tool • Develop cost-effective sampling strategies • Use of predictive models to optimize sampling (inter- and intra-field)

29. Objectives • Define accurate and cost-efficient sampling strategies to estimate GMO adventitious presence • Cost-effectiveness of sampling strategies: • Strategies defined without prior information about GMO AP (e.g., systematic sampling, random sampling, stratified sampling, transects sampling) • Strategies defined using prior information on within-field spatial distribution of GMO AP • Design pollen capture methods

30. Study Zone in Spain: 400 ha 226 Fields

31. Four imaginary transects Two edge zones (3 and 10 m wide) No detectable < 0.05 0.05 a 0.09 0.1 a 0.89 0.9 a 2 > 2 0.37 % Sampling Methodology Area weighted average Intersection samples % GMO estimated for each sub area averaging their vertex values Real-time PCR (qPCR) analysis

32. Model-based sampling strategies • Simulation of the within-field spatial distribution of GMO AP using a mathematical model (e.g., MAPOD) • Calculation of mean and variance of the simulated AP values. • Selection of representative sampling sites leading to mean and variance close to simulation values • Comparison of methods

33. Pollen capture (JRC-IHCP)

34. Map of the field trial in Girona

36. Activities in 2012 GM maize (MON 810) and non-GM maize pollen was monitored on the commercial farmer’s fields by using of passive sampling system developed by TIEM (Hofmann, 2004). Sampling duration was four weeks during which has been collected 20 pollen samples. All samples were delivered to the JRC IHCP EURL GMFF Ispra , Italy for molecular laboratory analysis. Molecular analysis of pollen samples is in progress with intention to use real-time PCR and digital PCR method, a ready-to-use multi-target analytical system for GM maize (pre-spotted plates) and next generation sequencing.