INSECT MODEL & SIMULATION 2016. 9. 27. Kyungsan Choi

Future DATA BASE Simulation

Target When How Expert System of Pest management Database - pest information - control information - experience data - simulated data Simulation : Prediction / Estimation - Occurrence time - Spraying time Target When How

Simulation in Insect model ∙ Theoretical base (Curry et al., 1978) ∙ Multi cohort simulation with a program (Wagner et al., 1985)

< Practical usage in Agriculture> ∙ 30 years later 1. Major researcher use Excel program - Problem : Takes long time, difficulty in building Model 2. Some use Simulation program - STELLA, DYMEX , etc - hardness in comprehension, restricted in specific model, etc 3. A few make a structural program by himself - Restricted for a model , low confidence, almost impossible to modify it < Practical usage in Agriculture> - Rare but in a few nation developed a system based on Degree day models. However, it does not well used because of the low accuracy

Why there is no platform for insect models ? Complication and variety in a model and its application

Is there easy way to build and simulate complicated models Program language X Variety STELLA DYMEX Difficulty Simple

X R Main C ∙ Template : STAGE MODEL - INPUT : X, R data - Main Function and Component Function X R Main C

∙ Various Method - Cal. Method - Array variable (I, N, T) ARRAY 1 TRANSITION

∙ Simple Stage linkage

PopModel 1.0 (2014)

PopModel 1.5 (2016)

Prediction System for Insect Pest (coming soon 2017) ☐ Forecasting : ∙ Predict the insect occurrence, damage period ∙ Suggest the timing of Spray ☐ Display structure ∙ Pest List according to crop ∙ Illustration of the occurrence and damage time ∙ Recommendation table : - protection time List for each pest - best insecticides and its best time to apply ☐ Management and Simulation engine ∙ Occurrence, damage, agro-chemical effective, and optimum timing Models are saved in Database as the model template of PopModel 1.5 ∙ Simulation run by the engine of the PopModel system

How to make insect occurrence model? ☐ Rate Distance R(T) = D/ T Rate = ----------- Time ☐ Insect development rate Insect are Poikilothermal animal => The development time is dependent on the temperature if The distance = 1 then dev. rate = 1/days (median or mean day) Temp day rate 16 4.9 0.203 20 2.7 0.364 24 1.8 0.545 28 1.6 0.620 32 1.3 0.799 35 0.568

1) Linear model : Y= a*X +b ☐ Degree-Day 1) Linear model : Y= a*X +b TL=10.3 DD = 27.6 Day Temp(T) If( T > TL, T, 0) Sum 1 8.8 2 9.5 3 6.4 4 14.5 4.46 5 22.5 12.46 16.9 6 19.3 9.26 26.1 7 38.6 8 18 7.96 46.6 Dev. Zero a = 0.0362 b = -0.3633 Dev. Zero = -b/a = 10.04 Thermal constant = 1/ a = 27.6

1. higher/lower estimation 2) Problem 1. higher/lower estimation higher estimation over optimal temp. Lower/higher estimation under Dev. Zero Dev. Zero is not a real but an abstract value deduced from linear model 2. No consideration on the variation of development Median : 21.4 <= Pos. at 50% Mean : 22.2 SE : 0.15 That’s why Degree-day is not failed in practical usage

☐ Non-linear model 1) Developmental rate Lactin 2 : Y=EXP(P0*X)-EXP(P0*P1-(P1-X)/P2)+P3 Estiamted value of parameter : P0=0.02198, P1 = 36.97 P2= 1.13, P3= - 1.196

2) Developmental complete distribution No. individual Dev. Period (days) 30 25 20 15 There are differences in variation and sample size from each temp treatments. So, How can we make a distribution model with those data?

Step 1. Convert them into cumulative probability Probabilty 20 30 25 15 Dev. Period (days) Step 2. Normalize the developmental period as dev. rate 1 dev. rate = 1/days So. We can say that 50% individuals complete their development when physiological age is 1 Cumulative Probabilty 1 2 Physiological age

3) Simulation method Pc : density of the cohort No. hatched larva 100 eggs Today ( N) data 1 2 3 4 5 6 7 8 9 N-1 of Px N of Px Pc : density of the cohort F(px) : developmental distribution model pxi : physiological age at ith day Pxi-1 Pxi

☐ Example : Stage emergence model - Examine the development of the target species at different constant temperature

- Make dev, rate and distribution model as well as degree-day model

- Simulate and validate model with field occurrence

☐ Example : Adult Oviposition model - Examine adult longevity , daily egg production

- Make 4 models

Adult longevity model Physiological age is calculated By summing the result of the model (1) Daily egg laid model (cumulative) Daily adult survival model Oviposition model of adult

☐ Example : Population model

Adult Larva Egg Pupa

Formula usually used in insect model ☐ Hilbert & Logan (1983) ☐ Lactin (1995) Lactin 1 model Lactin 2 model

☐ Gaussian (Taylor, 1981) ☐ Briere (Briere et al., 1999)

☐ SM model (Sharpe & DeMichele , 1977) ☐ SS model (Schoolfield, 1981) ☐ SSI model (Ikemoto, 2005)