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PREDICTING THE PROBABILITY OF PEST ESTABLISHMENT BY COMPARING SOURCE

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

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PREDICTING THE PROBABILITY OF PEST ESTABLISHMENT BY COMPARING SOURCE

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  1. 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

  2. Logistic Risk Curve

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

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

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

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

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

  8. China: Key to Hardiness Zones Zones Correspond to US hardiness zones

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

  10. ECOREGIONS OF THE WORLD (after BAILEY 1998)

  11. FVV, WORLD VEGETATION COVER

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

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

  14. Soil Climates of Canada - CANSIS

  15. Logistic Regression 100% Population Level (%)

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

  17. Benefits of Modelling • 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

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