Presented by Ryan Klafehn and Aaron Festinger Department of Mathematics

1. Suppressing Pest Resistance Evolution – When Refuges Go Bad Presented by Ryan Klafehn and Aaron Festinger Department of Mathematics University at Buffalo, The State University of New York Buffalo, NY 14260

2. Research Motivation • General motivation and the danger of resistance developing: • A genetically modified insecticidal cotton, Bt cotton, kills the primary pest, pink bollworm, that otherwise damages the crop • A gene for resistance to Bt cotton has been found in lab • The US Environmental Protection Agency (EPA) has mandated an integrated ‘refuge strategy’ • This promotes survival of pests that are susceptible to Bt cotton, delaying pest resistance S R SS RS RR • Fig. 1: Diploid species have two copies of each gene (1 from each parent); the gene has two variations (S is susceptible to the Bt cotton and R is resistant to the Bt cotton) resulting in the three possible genotypes for a pest 2

3. The Refuge Strategy Trying to mate with other RS pest Settles for mating with SS pest Refuge 3

4. Simpler Model Results • Previous modeling suggests that integrated refuges can be effective Log10 NR (red), NS (blue), total N (black) Time (Years) Time (Years) Fig. 3:0% integrated refuge Fig. 4:25% integrated refuge • Other recent modeling (ref. Ringland) suggests nearly isolated refuges can be more effective and lead to indefinite suppression 4

5. Simpler Models vs. Our Model Deterministic: kill 50% of individuals • Simpler models vs. Our model • Deterministic processes vs. demographic stochasticity • Static vs. stochastic weather • One-year time steps vs. one-day time steps • Unrealistic vs. highly detailed, realistic pest and agricultural modeling • Benefits of using our model: • Level of detail provides more actionable and trustworthy results Stochastic: kill each individual with probability 50% 0.125 multiple possible outcomes 0.375 0.375 (none) 0.125 with corresponding occurrence probabilities Fig. 5: Demonstration of the difference between deterministic and stochastic processes 5

6. Pest & Agricultural Modeling • Realistic pest modeling: • Complete pest development (egg-adult) and aging within pest stages based on weather • Diapause • Predation • Movement, mating, and egg laying (adult pests only) • Realistic agricultural modeling: • Cotton planting and development based on based on weather • Insecticide spraying (different poison than Bt; non-Bt fields only) • Bt-cotton killing (Bt fields only) • Stochasticity: • Binomial random variables used for realistic pest modeling • Random variables used for realistic agricultural modeling 6

7. Part I: Managing Pest Movement • Field 0 is toxic cotton • Field 1 is integrated refuge • Field 2 is nearly isolated refuge • Arrow 1 represents the complete mixing that occurs between the pest populations in Field 0 and Field 1 • Arrow 2 and Arrow 3 represent the same communication rate between Field 0 and Field 2 and between Field 1 and Field 2, respectively Field 0 1 1 - C 2 3 Field 1 C Field 2 Fig. 6: Spatial schematics of model 7

8. Model Complexity Field 0 Field 1 Animation 1: Typical pest population dynamics over a year Field 2 8

9. Simulation Results 0% integrated refuge = Good Outcome Fig. 7: Nearly isolated refuge with integrated refuge = 0% & communication rate = 0.005; Field 0, Field 1, Field 2, respectively, from top to bottom Results of 25 realizations 9

10. Simulation Results (cont’d) 13% integrated refuge = OK Outcome Fig. 8:Nearly isolated refuge with integrated refuge = 13% & communication rate = 0.005; Field 0, Field 1, Field 2, respectively, from top to bottom Results of 25 realizations 10

11. Simulation Results (cont’d) 25% integrated refuge = Bad Outcome Fig. 9:Nearly isolated refuge with integrated refuge = 25% & communication rate = 0.005; Field 0, Field 1, Field 2, respectively, from top to bottom Results of 25 realizations 11

12. Part II : Fully spatially explicit model Ref Fig. 10: 9 x 9 grid of toxic fields with a single centered refuge field Fig. 11: Movement probabilities for an insect in its natal field 12

13. Part II : Fully spatially explicit model RS SS RR Ref Ref Ref 100% 100% 100% 25% 25% 25% 0.25% 0% 6.25% Fig. 12: Maximum percentage of each genotype as a function of distance from the refuge 13

14. Part II : Fully spatially explicit model Fig. 13 14

15. Part II : Fully spatially explicit model Fig. 14a: Illustration of resistance formation due to movement from refuge Fig. 14b: Illustration of resistance formation from initial populations 15

16. Part II : Fully spatially explicit model Year 110 Year 111 Animation 2: Resistance forming from refuge population in 117 years 16

17. Acknowledgements & References • Research group members: • John Bantle, Ryan Klafehn, Aaron Festinger, and Hee-Joon Jo of the University at Buffalo • Research mentor: • Dr. John Ringland of the University at Buffalo • References: • The Arizona Meteorological Network (AZMET) • Kiraly and Janosi, Stochastic modeling of daily temperature fluctuations, 2002 • Ringland, et al., A situation in which local nontoxic refuge promotes pest resistance to toxic crops, 2007 • Sisterson, et al., Effects of Insect Population Size on Evolution of Resistance to Transgenic Crops, 2004 17