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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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Integrating prevention and control of invasive species the case of the brown treesnake l.jpg

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


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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


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Boiga irregularis


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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*


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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)


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Algorithm to minimize cost + damage

=> V* => Nc*


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PV costs + damage ifNc* < Nmin

  • If N*c <Nmin, we must then consider the costs of preventing re-entry.

Z =


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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:


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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)


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Choose optimal population

  • If N* Nmin, same as existing invader case

    • Control only

  • If N* < Nmin,

    • Iterative prevention/removal cycle


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Case study: Hawaii

  • Approximately how many snakes currently reside in Hawaii?

  • Conversations with expert scientists: between 0-100


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Growth

  • Logistic: b=0.6, K=38,850,000


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Damage

  • Power outage costs: $121.11 /snake

  • Snakebite costs: $0.07 /snake

  • Biodiversity: $0.32 – $1.93 /snake

  • Total expected damages:


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BiodiversityLosses


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Control cost

  • Catching 1 out of 1: $1 million

  • Catching 1 out of 28: $76,000

  • Catching 1 out of 39m: $7


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Probability of arrival a function of spending


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Results

  • Aside from prevention, eradicate to zero and stay there.

  • Since prevention is costly, reduce population from 28 to 1 and maintain at 1


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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


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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


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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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