2. PREDICTING THE PROBABILITY OF PEST ESTABLISHMENT BY COMPARING SOURCE AND DESTINATION ENVIRONMENTS by Dr. Erhard John Dobesberger, Plant Health Risk Assessment Unit, Ottawa, Canada K2H 8P9 PREDICTING THE PROBABILITY OF PEST ESTABLISHMENT BY COMPARING SOURCE AND DESTINATION ENVIRONMENTS by Dr. Erhard John Dobesberger, Plant Health Risk Assessment Unit, Ottawa, Canada K2H 8P9

4. Logistic Risk Curve

5. Y = [1 + exp(-ß1 - ß2*X)] Pest or Disease Progress Curve

6. Risk Curve HIGH MEDIUM LOW EXPECTED DAMAGE LEVEL (%)

7. CLIMATIC FACTORS Temperature - minimum, maximum etc. Moisture - rainfall, snow, relative humidity Radiation - solar Wind - wind speed Pressure - vapour, atmospheric evapotranspiration, daylength

8. Modelling Methodologies process oriented models expert systems - artificial intelligence all of the above - integrated models Ecoclimatic zone comparison Ecoclimatic zone comparison Simple geographic mapping themes Simple geographic mapping themes multivariate – logistic models multivariate – logistic models

10. Hardiness zones in Canada which correspond to US hardiness zones of North America

11. China:KeytoHardinessZonesZonesCorrespond to US hardinesszones

12. Hardiness zones in Canada which correspond to US hardiness zones of North America

15. ECOREGIONS OF THE WORLD (after BAILEY 1998)

16. VEGETATION FVV, WORLD VEGETATION COVER

17. Huke: Agroclimatology for South, Southeast, and East Asia, Length of Dry and Wet Seasons

18. Ecodistricts of Canada - 1961 - 1990 Climatic Normals http://sis.agr.gc.ca/cansis/

21. Soil Climates of Canada - CANSIS Soil Climates of Canada - CANSIS

27. Logistic Regression 100% Population Level (%)

28. Probability of establishment by Pectinophora gossypiella in the USA From Venette and Hutchison (1999)

29. Internationally accepted sound scientific basis - standard prediction for massive data sets Powerful, versatile forecasting and transparent decision-support tool better communication of risk scenarios stimulus for new research and understanding should aid in superior phytosanitary resource allocation Internationally accepted sound scientific basis - standard prediction for massive data sets Powerful, versatile forecasting and transparent decision-support tool better communication of risk scenarios stimulus for new research and understanding should aid in superior phytosanitary resource allocation Benefits of Modelling