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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

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
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
methodology
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*
slide5

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)

pv costs damage if n c n min
PV costs + damage ifNc* < Nmin
  • If N*c <Nmin, we must then consider the costs of preventing re-entry.

Z =

prevention eradication cycle
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:
slide9

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)

choose optimal population
Choose optimal population
  • If N* Nmin, same as existing invader case
    • Control only
  • If N* < Nmin,
    • Iterative prevention/removal cycle
case study hawaii
Case study: Hawaii
  • Approximately how many snakes currently reside in Hawaii?
  • Conversations with expert scientists: between 0-100
growth
Growth
  • Logistic: b=0.6, K=38,850,000
damage
Damage
  • Power outage costs: $121.11 /snake
  • Snakebite costs: $0.07 /snake
  • Biodiversity: $0.32 – $1.93 /snake
  • Total expected damages:
control cost
Control cost
  • Catching 1 out of 1: $1 million
  • Catching 1 out of 28: $76,000
  • Catching 1 out of 39m: $7
results
Results
  • Aside from prevention, eradicate to zero and stay there.
  • Since prevention is costly, reduce population from 28 to 1 and maintain at 1
snake policy status quo vs optimal win win

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

summary
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
uncertainties
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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