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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. - PowerPoint PPT Presentation
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PREDICTING THE PROBABILITY OF PEST PREDICTING THE PROBABILITY OF PEST
ESTABLISHMENT BY COMPARING SOURCE ESTABLISHMENT BY COMPARING SOURCE
AND DESTINATION ENVIRONMENTSAND DESTINATION ENVIRONMENTS
byby
Dr. Erhard John Dobesberger, Dr. Erhard John Dobesberger, Plant Health Risk Assessment Unit, Plant Health Risk Assessment Unit,
Ottawa, Canada K2H 8P9Ottawa, Canada K2H 8P9
PREDICTING THE PROBABILITY OF PEST PREDICTING THE PROBABILITY OF PEST
ESTABLISHMENT BY COMPARING SOURCE ESTABLISHMENT BY COMPARING SOURCE
AND DESTINATION ENVIRONMENTSAND DESTINATION ENVIRONMENTS
byby
Dr. Erhard John Dobesberger, Dr. Erhard John Dobesberger, Plant Health Risk Assessment Unit, Plant Health Risk Assessment Unit,
Ottawa, Canada K2H 8P9Ottawa, Canada K2H 8P9
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0 10 20 30 40 50 70 90
Expected Damage Level (%)
Cu
mu
lati
ve P
rob
abili
tyLogistic Risk CurveLogistic Risk Curve
-1Y = [1 + exp(-ß1 - ß2*X)]
Pest or Disease Progress CurvePest or Disease Progress Curve
00.10.20.30.40.50.60.70.80.9
1
0 15 30 45 70 100
Time or Environmental Indicator
% P
op
ula
tio
n D
en
sit
y
NORMAL ABUNDANCEOCCASIONAL ABUNDANCEPOSSIBLE ABUNDANCE
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Expected Risk Level
Cum
ulat
ive
Pro
babi
lity Risk Curve
HIGH
MEDIUMLOW
EXPECTED DAMAGE LEVEL (%)EXPECTED DAMAGE LEVEL (%)
CLIMATIC FACTORSCLIMATIC FACTORS
• Temperature - minimum, maximum etc.• Moisture - rainfall, snow, relative humidity• Radiation - solar• Wind - wind speed• Pressure - vapour, atmospheric
• evapotranspiration, daylength
Modelling MethodologiesModelling Methodologies
• process oriented models• expert systems - artificial intelligence
• all of the above - integrated models
• Ecoclimatic zone comparisonEcoclimatic zone comparison• Simple geographic mapping themesSimple geographic mapping themes
• multivariate – logistic models multivariate – logistic models
Hardiness zones in Canada which correspond Hardiness zones in Canada which correspond to US hardiness zones of North Americato US hardiness zones of North America
China: China:
Key Key
to to
HardinessHardiness
ZonesZones
Zones Zones
CorrespondCorrespond
to US to US
hardiness hardiness
zoneszones
Hardiness zones in Canada which correspond Hardiness zones in Canada which correspond to US hardiness zones of North Americato US hardiness zones of North America
ECOREGIONS OF THE WORLD (after BAILEY 1998)ECOREGIONS OF THE WORLD (after BAILEY 1998)
FVV, WORLD VEGETATIONVEGETATION COVER
Huke:Huke: Agroclimatology for South, Southeast, and East Asia, Length of Dry Agroclimatology for South, Southeast, and East Asia, Length of Dry
and Wet Seasonsand Wet Seasons
Ecodistricts of Canada - 1961 - 1990 Climatic Normals Ecodistricts of Canada - 1961 - 1990 Climatic Normals
http://sis.agr.gc.ca/cansis/
Soil Climates of Canada - CANSIS Soil Climates of Canada - CANSIS
VPJUN Mean vapour pressure in June, mb
N2200 Number of days required to reach 2200 Corn Heat Units, CHU
VAP Mean vapour pressure during the growing season
RAINMAY Mean rainfall in May, mm
RAINAUG Mean rainfall in August, mm
DLMAR Mean day length in March, hr/day
DLOCT Mean day length in October, hr/day
RAINJUL Mean rainfall in July, mm
SNOWOCT Mean snowfall in October, cm
SNOWNOV Mean snowfall in November, cm
PENOV Mean potential evapotranspiration in November, mm/day
SNOWMAY Mean snowfall in May, cm
TMAXJUL Maximum temperature in July, ooC.
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0 10 20 30 40 50 70 90
Expected Damage Level (%)
Cu
mu
lati
ve P
rob
abili
ty
Logistic RegressionLogistic Regression100%
Population Level (%)Population Level (%)
Probability of establishment by Probability of establishment by Pectinophora gossypiellaPectinophora gossypiella in the USA in the USA
From Venette and Hutchison (1999)
• 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 ModellingBenefits of ModellingBenefits of ModellingBenefits of Modelling
