El Granulovirus de Phthorimaea operculella PoGV - PowerPoint PPT Presentation

Title: El Granulovirus de Phthorimaea operculella PoGV


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Insect life cycle modeling (ILCYM) software its
application and use a new modeling toolbox
Marc Sporleder Daniel Chavez Juan Gonzales Henry
Juarez Reinhard Simon Jürgen Kroschel
CIP ANNUAL REVIEW AND MEETINGOctober 27
November 7, 2008 Lima, Peru
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Output Target
Division 4 Output 4 Components and strategies
for the integrated management of key potato and
sweetpotato insect pests developed, tested and
disseminated as part of ICM strategies in LAC,
SSA and Asia priority countries (5 to 8 years).
Target 6 (2007)Statistical package for
analyzing and simulating potato pest phenology,
as well as for forecasting the regional
distribution potentials using GIS available to
support decision-making related to IPM. The
package could be applicable to any pest in any
crop.
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Steps for developing a phenology model
Design the model
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How you want to use the model?
Collect the data
Temperature experiments, literature
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Analyze the data
Define functions describing temperature-driven
processes in insect development
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Compile the model
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with additional data that were not included for
developing the model (generally this data are
from experiments conducted under fluctuating
temperatures)
Validate the model
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Sensitivity analysis
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Use the model
p.e. for pest risk mapping, pest management,
etc.
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Biological Modelling of Insect Species
Creative Research Systems
Su et al. (2002) Stephens Dentener (2005) Yonow
et al. (2004)
US 1,950
US 2,250
Sutherst et al. 1991, 1999, 2000
North Carolina State University
Borchert Magarey 2005 Nietschke et al. 2008
Simulistics Ltd.
Simile v5.3, Standard Edition - US 595 Stella,
ModelMaker, PowerSim
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Model Implementation
Rate summation and a cohort up-dating approach
Total oviposition
Relative oviposition frequency
Reproduction
Development to the next stage
aging
EI
E..
E1
Female rate 0.5
E0
LI
L..
L1
PI
L0
P..
P1
AfI
P0
Af..
Mortality
Af1
Af0
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Model Implementation

Stochastic simulation of life tables
Total oviposition
Relative oviposition frequency
?
Reproduction
Development to the next stage

aging
EI
E..

E1
Female rate 0.5
E0
LI
L..
L1
PI
L0
P..
P1
AfI
P0
Af..
Mortality
Af1
Af0

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ILCYM the Model builder
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ILCYM the Model builder
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ILCYM Model builder
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ILCYM Model builder
Variation in development time between individuals
Daniel, este debe ser mas grande
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ILCYM Model builder
Variation in development time between individuals
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ILCYM Model builder
Development time in relation to temperature
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ILCYM Model builder
Development time in relation to temperature
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ILCYM Model builder
Mortality in relation to temperature
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ILCYM Model builder
Mortality in relation to temperature
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ILCYM Model builder
Fecundity total per female in relation to
temperature
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ILCYM Model builder
Fecundity total per female in relation to
temperature
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ILCYM Model builder
Relative oviposition in relation to female age
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ILCYM Model builder
Relative oviposition in relation to female age
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ILCYM Model builder
Compiling the model
Validating the model
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ILCYM Model builder
Validating the model
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ILCYM Model builder
Validating the model
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ILCYM Model builder
Simulations
EAI
Establishment risk index (Survival risk
index) Index (1-xEgg) ? (1-xLarva) ?
(1-xPupa) x is percentage of days a specific
life-stage does not survives
GI
Generation Index the number of generations that
may be produced within one
Activity Index Index Log ? finite rate of
population increase (?)
AI
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ILCYM Model builder
Stochastic simulation of life tables
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ILCYM Model builder
Stochastic simulation of life tables
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ILCYM Model builder
Deterministic simulation of populations
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ILCYM Model builder
Deterministic simulation of populations
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ILCYM Pest Risk Mapping
Geographic Simulation
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ILCYM Pest Risk Mapping
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ILCYM Pest Risk Mapping
Ventana de colores
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ILCYM Pest Risk Mapping
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Modeling in IPM Research
Repeated stochastic simulation of life table
parameters over a range of temperatures.
Phthorimaea operculella
Symmetrischema tangolias
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Modeling in IPM Research
Evaluating the potential of parasitoids for
biological control.
Copidosoma koehleri
Phthorimaea operculella
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Modeling in IPM Research
Global warming and its effects on pest populations
CCM3 model according to Govindasamy, B., P. B.
Duffy, J. Coquard, 2003. High-resolution
simulations of global climate, part 2 effects of
increased greenhouse cases. Climate Dynamics 21
391404.
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Modeling in IPM Research
Generation index change for Phthorimaea
operculella by 2050
CCM3 model according to Govindasamy, B., P. B.
Duffy, J. Coquard, 2003. High-resolution
simulations of global climate, part 2 effects of
increased greenhouse cases. Climate Dynamics 21
391404.
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Modeling in IPM Research
Effects of PoGV applications on P. operculella
field populations
Simulated natural populations increase of P.
operculella at 24ºC (bold line) Four scenarios
are simulated 1 a single application of
51013 OB/ha (bold scattered line), 2 two
applications of 51013 OB/ha with the second
application 7 days after the first (timid
scattered line), 3 a series of 5 applications
of 1013 OB/ha in 7-day intervals (grey solid
line), and 4 a series of 10 applications of
1013 OB/ha in 7-day intervals (light grey solid
line).
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Conclusions
ILCYM provides advanced insect modeling
techniques and analysis tool that can be used
efficiently by NARS scientists which are not
experts in in this field. The ILCYM
interactively leads the user through the steps of
developing a Pest Population Model and aids
conducting spatial simulations Users dont need
to learn programming languages However, ILCYM
restricts the modeler to certain modeling
approaches (model designs) and might not provide
solutions for every problem.
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Thanks to Daniel Chavez Juan Gonzales Henry
Juarez Reinhard Simon Felipe Mendiburu Jürgen
Kroschel
Thanks