Modelling the Spread of Sudden Oak Death in the UK

1. February 2012 Modelling the Spread of Sudden Oak Death in the UK Richard Stutt Nik Cunniffe Erik DeSimone Matt Castle Chris Gilligan

2. Contents • Example results from landscape-scale models • SOD in California (precursor to this model) • SOD in UK • How the model works • Host landscape • Environmental conditions • Pathogen dispersal • Uses of the model • Predictions of spread • Effects of control

3. PredictedSpread in California

4. Predictions for the UK 2010-2020

5. How the Model Works

6. Stochastic Spatial Model • Key components: • Host • Environment • Pathogen dynamics and dispersal • Expressed as a compartmental model

7. Model - Host • Susceptible hosts in the landscape are divided into a metapopulation at a chosen resolution (250m) • UK Sudden Oak death landscape assembled from: • National Inventory of Woodland Trees (NIWT) • Forestry Commission commercial Larch data • Maximum Entropy suitability models for Rhododendron and Vaccinium (FERA/JNCC) • Different hosts have different weightings for sporulation and susceptibility

8. Broadleaved Coniferous Young Trees Felled Construction of Host Landscape

9. Final HostLandscape

10. Model - Environment • Identify favourable conditions for P. ramorum • moisture • temperature • Parameterise using experimental results Relative Sporulation Temperature

11. Model - Environment • Calculate underlying suitability of locations in the landscape • Statistical used to model future conditions

12. Model – Dispersal • Dispersal kernel is a statistical description of transport of inoculum between locations • Implicitly incorporates many mechanisms

13. Model - Validation • Fit model using historic spread data • Used Maximum Likelihood to assess goodness of fit • Predicted probability of infection by 2010 given starting conditions in 2004 Survey Positive for P. ramorum Survey Negative for P. ramorum

14. Typical applications of the model • Prediction in the absence of control • Effect of controls • Felling infected stands • Felling infected stands + proactive control • Effect of any delay in implementing control • Application to surveying for P. Ramorum

15. Probability of Infection

16. Risk – Reactive Control

17. Risk – Proactive Control (250m)

18. Risk Update – 20 year horizon

19. Disease Progress – No Control

20. Total Infection Symptomatic Symptomatic at time of Survey Disease Progress – Stand Control

21. Total Infection Symptomatic Symptomatic at time of Survey Disease Progress – 100m Radius

22. Total Infection Symptomatic Symptomatic at time of Survey Disease Progress – 250m Radius

23. Total Infection Symptomatic Symptomatic at time of Survey Disease Progress – 500m Radius

24. Disease Progress – Comparisons

25. Effects of Delay Before Culling Examine region of South Wales

26. Effect of Delay Before Culling Cull: no delay after survey 6 month delay

27. Sampling Strategies • Key Questions When Surveying for Disease: • Where is the disease likely to be? • Where is it likely to be most severe and spread most rapidly? • How to optimise the sampling?

28. Sampling Strategies • Uses: • Currently known outbreaks • Predicted severity of outbreaks • => Sampling weighting • Survey pattern formed • => sampling from weightings • Map shows a weighting and a set of survey points (green)

29. Future Work • Continue to improve the model • Refinement of country wide strategies: • Region specific control • Effect of non compliance • User friendly models

30. Acknowledgements • Frank van den Bosch, Stephen Parnell • Rothamsted Research • Forestry Commission, FERA • (in particular Bruce Rothnie and Keith Walters) • Funding from DEFRA, BBSRC and USDA