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Integrating Prevention and Control of Invasive Species: The Case of the Brown Treesnake

Integrating Prevention and Control of Invasive Species: The Case of the Brown Treesnake. Kimberly Burnett, Brooks Kaiser, Basharat A. Pitafi, James Roumasset University of Hawaii, Manoa, HI Gettysburg College, Gettysburg, PA. Objectives.

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Integrating Prevention and Control of Invasive Species: The Case of the Brown Treesnake

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  1. Integrating Prevention and Control of Invasive Species: The Case of the Brown Treesnake Kimberly Burnett, Brooks Kaiser, Basharat A. Pitafi, James Roumasset University of Hawaii, Manoa, HI Gettysburg College, Gettysburg, PA

  2. Objectives • Illustrate dynamic policy options for a highly likely invader that has not established in Hawaii • Find optimal mix of prevention and control activities to minimize expected impact from snake

  3. Boiga irregularis

  4. Methodology • First consider optimal control given N0 (minimized PV of costs and damages) =>Nc* • We define prevention to be necessary if the population falls below Nmin (i.e., Nc*< Nmin) • Determine optimal prevention expenditures (to decrease probability of arrival) conditional on the minimized PV from Nc*

  5. N0 ≥Nmin Nc* = Best stationary N without prevention Nc* Nmin Nc*<Nmin We have a winner! N* = Nc* Choose y to min cost of removal/prevention cycle Z(Nc*) V(Nmin) N* = Min (Z,V)

  6. Algorithm to minimize cost + damage => V* => Nc*

  7. PV costs + damage ifNc* < Nmin • If N*c <Nmin, we must then consider the costs of preventing re-entry. Z =

  8. Prevention/eradication cycle • Expected present value of prevention and eradication: • p(y): probability of successful introduction with prevention expenditures y. Minimizing Z wrt y results in the following condition for optimal spending y:

  9. N0 ≥Nmin Nc* = Best stationary N without prevention Nc* Nmin Nc*<Nmin We have a winner! N* = Nc* Choose y to min cost of removal/prevention cycle Z(Nc*) V(Nmin) N* = Min (Z,V)

  10. Choose optimal population • If N* Nmin, same as existing invader case • Control only • If N* < Nmin, • Iterative prevention/removal cycle

  11. Case study: Hawaii • Approximately how many snakes currently reside in Hawaii? • Conversations with expert scientists: between 0-100

  12. Growth • Logistic: b=0.6, K=38,850,000

  13. Damage • Power outage costs: $121.11 /snake • Snakebite costs: $0.07 /snake • Biodiversity: $0.32 – $1.93 /snake • Total expected damages:

  14. BiodiversityLosses

  15. Control cost • Catching 1 out of 1: $1 million • Catching 1 out of 28: $76,000 • Catching 1 out of 39m: $7

  16. Probability of arrival a function of spending

  17. Results • Aside from prevention, eradicate to zero and stay there. • Since prevention is costly, reduce population from 28 to 1 and maintain at 1

  18. First period cost Annual cost PV costs Annual damages NPV damages PV losses Status quo $2.676 m $2.676 m $133.8 m $4.5 b $145.9 b $146.1 b Opt. policy $2.532 m $227,107 $13.88 m $121 $9,400 $13.89 m Snake policy: status quo vs. optimal (win-win) NPV of no further action: $147.3 billion

  19. Summary • Re-allocation between prevention and control may play large role in approaching optimal policy even at low populations • Eradication costs increased by need for prevention, which must be considered a priori • Catastrophic damages from continuation of status quo policies can be avoided at costs much lower than current spending trajectory

  20. Uncertainties • Range of snakes currently present (0-100?) • 8 captured • More may’ve gotten away • Not much effort looking • Probability of reproduction given any pop’n level • Don’t know, need to look at range of possibilities • Here all control • If N*<Nmin, prevention makes sense • Need to find optimal mix

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