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GMBI
General Model Biological Invasion Why we need a GMBI for biosecurity Why GMBI’s flexibility is critical How GMBI works How we been testing GMBI’s flexibility
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Putting the GMBI to the test... Michael Renton, David Savage, Nancy Shackelford, Nancy Schellhorn, Jerome Chopard , James Bennett, Art Diggle
biosecurity built on science 5 Cooperative Research Centre for National Plant Biosecurity
The problem
New biological invasion Try to eradicate or not?? Rapid response critical Lack of information about extent of invasion & organism - No historic data
Very costly to delay or get it wrong biosecurity built on science 6
To enable rapid response Gather experts.
Characterise organism. New plant pest or disease organism found!
Strategy identified.
Predictions analysed and synthesised
Information gathered used in modelling framework. Simulations of organism spread carried out. biosecurity built on science 7
What should the model look like? How much detail to include? How complex? How specific?
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Little knowledge No knowledge in this situation Little data No data in this situation Probably no model Sure no model in this situation
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Little knowledge No knowledge in this situation Little data No data in this situation Probably no model Sure no model in this situation
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And then the model tells us:
Too late for eradication!
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Have a model ready! biosecurity built on 12 science
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GMBI
General Model of Biological Invasion Main drivers:
1. Population dynamics at a location 2. Dispersal between locations 3. Landscape suitability characteristics 4. (Surveillance and detection) biosecurity built on 15 science
GMBI
General Model of Biological Invasion Dispersal kernel Leptokurtic Anisotropic Active dispersal
Life stages Age structured dispersal, Mortality, reproduction‌
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Organism Parameters Biologically meaningful - helps get values from experts
First specify life stages Then mortality, reproduction, dispersal etc for each life stage
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Landscape Can be specified using GIS data or on remote sensed images Or more generally through ‘library’ of statistically generated landscapes
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Stochastic‌ many runs can give risk maps
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 Currently testing for some real organisms (plant, fungus, insect)  For some real applications
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Nessy  Nesaecrepida Infuscata
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Ehrharta calycina
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Colletotrichum lupini
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The whole framework Gather experts.
Characterise organism. New plant pest or disease organism found!
Strategy identified.
Predictions analysed and synthesised
Information gathered used in general modelling framework. Simulations of organism spread carried out. biosecurity built on 37 science
Where we are going... Sensitivity analysis - Which parameters really matter?
Meta-modelling - Even simpler models of the GMBI
Surveillance, detection - Estimate time to detection
Model updating, Management Transport Networks? biosecurity built on 38 science
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General Modelling Framework for Rapid Response  Drive for abstraction/simplicity/empiricism
- Generality / Flexibility (species, kingdoms, agri vs natural) - Transparency and ease of analysis - Computational efficiency
 Drive for complexity/mechanism/detail -
Biological realism Facilitates parameterisation (by experts) Include all important processes Explore and understand processes Prediction in new situations (extrapolation) biosecurity built on 40 science
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