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Managing Resistance Evolution with Refugia

Managing Resistance Evolution with Refugia

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Managing Resistance Evolution with Refugia

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  1. Managing Resistance Evolution with Refugia Livingston, Carlson and Fackler Presented by Ben Crost

  2. The Setting • Cotton production in the midsouth • Two pests: budworm and bollworm • Two pest-control technologies: Bt-cotton and pyrethroids • Evolution of resistance to pest-control is a major problem

  3. The Setting (2) • Resistance-free pests are a public good • The EPA tries to control resistance by mandating refugia • Farmers have two options: • 1.) leave 5% of their cotton-crop non-Bt and unsprayed • 2.) leave 20% non-Bt but sprayed

  4. The Question • What is the optimal size of refugia? • Combine biological, economic and regulatory model

  5. Biological Model • 2-locus by 2-allele model • Locus: The specific place on a chromosome where a gene is located • Allele: A variant of the DNA sequence at a given locus

  6. Biological Model (2) • 2 Loci: Bt-resistance, Pyrethroid resistance • 2 Alleles: resistant, non-resistant • This setup gives rise to 9 different genotypes (since each individual has 2 sets of chromosomes)

  7. Biological Model (3) • 5 non-overlapping generations • Genes get transmitted between generations by random mating • (Calculate frequencies of all 4 possible gametes and then frequencies of all 9 possible combinations)

  8. Biological Model (4) • Genotypes can be confronted with 4 possible environments (Bt/non-Bt by sprayed/unsprayed) • Each genotype has a survival-probability in each environment • Given the environments, we know what will happen to the pest-population

  9. Economic Model • Representative producer maximizes profits, s.t. pest-population and regulatory constraints • Size of pest-population maps into Bt-use, Pyrethroid-use and profits • Bt-use and Pyrethroid-use feed back into biological model

  10. Regulatory Model • Regulators want to choose refuge constraints that maximize the representative producers discounted profits • 2 Scenarios: Static and dynamic

  11. Estimation • Lots of parameters from a variety of sources (lab-studies, econometric estimation from observed data, educated guesses from observed data) • Grid search over possible refugia sizes

  12. Results • Current refugia mandates are too large (optimal would be 2% unsprayed or 16% sprayed) • Results are very sensitive to heterozygous Bt-resistance parameter (up to 74% sprayed refugia with still realistic parameters)

  13. Remarks • It seems that the authors were aiming for a low value of refugia: • They chose a short time-horizon and no bequest value • Their values for heterozygous resistance are lower than lab-studies suggest

  14. Improvements? • Get better estimates of model parameters • Calibrate model to observed data • Bayesian Model Averaging