beyond the bonus: reuniting and adapting to …beyond the bonus: reuniting and adapting to...

Background

MotivatingCase Study

Model

Results

Conclusions

Beyond the Bonus: Reuniting and Adapting toFragmented Habitat for Species Recovery

Jacob Hochard1, Yuanhao “Jack” Li2, David Finno↵3 and Jason Shogren31East Carolina University 2Norwegian School of Economics

3University of Wyoming

We are grateful for funding received under a 2016CBEAR mini-grant

Background

MotivatingCase Study

Model

Results

Conclusions

Preview:

• Wildlife managers adapt to fragmented habitat.

• Modelled using reverse auction with a responsive budget.

• Addresses two identified shortcomings to reverse auctionmechanism.

– Low turnout of farmers can reduce cost-e↵ectiveness ofprogram (Palm-Forster et al. 2016 AJAE).

– Greater heterogeneity in seller opportunity costs reducesfiscal e�ciency of program (Messer et al. 2017 EE).

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Landscape ecology:

• Higher carrying capacity

• Higher biodiversity

• Improved species resilience

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Landscape ecology:

• Higher carrying capacity

• Higher biodiversity

• Improved species resilience

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Captive breeding programs:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Captive breeding programs:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Captive breeding programs:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Captive breeding programs:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Captive breeding programs:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Predator control:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Predator control:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Predator control:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Predator control:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Predator control:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Grazing reductions:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

Invasives management:

Background

MotivatingCase Study

Model

Results

Conclusions

Why do we strive to construct contiguous habitat?

Management of wildlife populations is typically more expensivewhen habitat is fragmented

(Moilanen and Wintle 2007, Conservation Biology).

See larger review on human-carnivore conflict:

Background

MotivatingCase Study

Model

Results

Conclusions

The eastern NC Red Wolf Recovery Program

2000 2002 2004 2006 2008 2010 2012 2014 2016

0.5

1

1.5·106

Year

Allocatedbudget

(millionsofdollars

$)

50

100

150

Estim

atedRed

WolfPopulation

Budget

Population

Background

MotivatingCase Study

Model

Results

Conclusions

The eastern NC Red Wolf Recovery Program

A Comprehensive Review and Evaluation of the Red Wolf Recovery Program“More intensive monitoring of wolves will be required in order to respond to the

public concerns about their movement and habitat use. Alternatively, expenses

could decrease if the FWS would adopt a di↵erent management strategy that would

allow wolves to disperse throughout the restoration area. This action would likely

be tolerated only if the FWS developed a landowner incentive program that would

provide compensation for the presence of red wolves on private property within the

restoration area. Funding levels must consider the red wolf/coyote hybridization

issue that is likely to occur in any selected reintroduction site...” (WMI 2014).

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Traditional mechanisms focus on mitigating fragmentedhabitat...

rather than adapting to its presence.

Two shortcomings:

• Unequal payouts may be more equitable when privatesentiments influence opportunity cost of enrollment.

• Mutually agreeable payouts avoid systematic overpaymentsto “green” landowners and underpayments to “brown”landowners.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Traditional mechanisms focus on mitigating fragmentedhabitat...rather than adapting to its presence.

Two shortcomings:

• Unequal payouts may be more equitable when privatesentiments influence opportunity cost of enrollment.

• Mutually agreeable payouts avoid systematic overpaymentsto “green” landowners and underpayments to “brown”landowners.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Traditional mechanisms focus on mitigating fragmentedhabitat...rather than adapting to its presence.

Two shortcomings:

• Unequal payouts may be more equitable when privatesentiments influence opportunity cost of enrollment.

• Mutually agreeable payouts avoid systematic overpaymentsto “green” landowners and underpayments to “brown”landowners.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Traditional mechanisms focus on mitigating fragmentedhabitat...rather than adapting to its presence.

Two shortcomings:

• Unequal payouts may be more equitable when privatesentiments influence opportunity cost of enrollment.

• Mutually agreeable payouts avoid systematic overpaymentsto “green” landowners and underpayments to “brown”landowners.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Traditional mechanisms focus on mitigating fragmentedhabitat...

rather than adapting to its presence.

Addresses both shortcomings.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Traditional mechanisms focus on mitigating fragmentedhabitat...rather than adapting to its presence.

Addresses both shortcomings.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Traditional mechanisms focus on mitigating fragmentedhabitat...rather than adapting to its presence.

Addresses both shortcomings.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs

Comparing Parkhurst et al. (2002) and Fooks et al. (2016)using a Nash bargaining space.

0x + AB

x + AB

Budget=y

PD

i

Extended bargainingfrontier with AB in-corporated in budget

Payo↵ expansion pathin AB mechanism

E�cient frontier inan auction with AB

ThreatPoint

Player 1 payo↵

Player 2 payo↵

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs: Incorporating a

responsive Pareto-e�cient frontier.

The government’s decision rule

maxD

� ·5X

i=1

D

i

subject to

5X

i=1

B

i

�mN(D) y ·X

i=1

D

i

Parameter treatments

T1 T2 T3

C1 25 25 20C2 25 25 150C3 25 25 20C4 25 300 150C5 25 25 150y

100 100 100m

20 20 10

c

i

private opportunity cost, y per-parcel budget, m management cost

Two information treatments: y ,m known and unknown to participants.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs: Incorporating a

responsive Pareto-e�cient frontier.

The government’s decision rule

maxD

� ·5X

i=1

D

i

subject to

5X

i=1

B

i

�mN(D) y ·X

i=1

D

i

1 3

X X

XX

X X

Budget: 2y � 6m

1

5

X X

XX

X

X

X X

Budget: 2y � 8m

c

i

private opportunity cost, y per-parcel budget, m management cost

Two information treatments: y ,m known and unknown to participants.

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs: Incorporating a

responsive Pareto-e�cient frontier.

0C1 + C2 y

PD

i

� mN(D)

C3 + C4 + C5

y

PD

i

� mN(D)

y

PD

i

y

PD

i

Least-cost management arising

from optimal coordination, mN(D)

Threat Point assumingPB

i

� mN(D) y

PD

i

Pareto Frontier in theabsense of management costs

E↵ective Pareto FrontierassumingP

B

i

� mN(D y

PD

i

E↵ective

Pareto

Bargaining Space

Player 1 payo↵

Player 2 payo↵

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs: Treatment 1.

0 25 40 80 160

25

2y � 8m = 40

50

2y � 6m = 80

3y � 7m = 160

A

B

Threat points of retiring

1&3 and 1&5

1, 3&4

Gov. budget for squares

1&5

1&3

1, 3&4

Player 1 payo↵

Player 2 payo↵ 1 3X X

XX

X X

Budget: 2y � 6m

1

5X X

XX

X

X

X X

Budget: 2y � 8m

1 34X

X

X

X

X

X

X

Budget: 3y � 7m

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs: Treatment 2.

0 50 220 320

50

4y � 9m = 220

5y � 9m = 320

350

C

Threat points of retiring

1, 2, 3&5

1, 2, 3, 4&5

Gov. budget for squares

1, 2, 3&5

1, 2, 3, 4&5

Player 1 payo↵

Player 2 payo↵

1 3

52

X

X

X

X

X

X

X

X

X

Budget: 4y � 9m

1 3425

X

X

X

X

XX

X

X

X

Budget: 5y � 9m

Background

MotivatingCase Study

Model

Results

Conclusions

Landowner incentive programs: Treatment 3.

0 20 140 170 230

20

2y � 6m = 140

170

3y � 7m = 230

D

E

F

Threat points of retiring

1&3

1, 2&3

1, 3&4

Gov. budget for squares

1&2

1, 2&3/1, 3&4

Player 1 payo↵

Player 2 payo↵

1 3X X

XX

X X

Budget: 2y � 6m

12

3X X

X

X

X

X

X

Budget: 3y � 7m

1 34X

X

X

X

X

X

X

Budget: 3y � 7m

Background

MotivatingCase Study

Model

Results

Conclusions

Treatments 1 & 2: Given time, participants converge to socially-optimal

enrollment.

Marginal e↵ects

Contiguous Fragmented

Full Info.0.005⇤⇤ 0.011⇤⇤⇤

(0.002) (0.001)

No Info.0.010⇤⇤⇤ 0.015⇤⇤⇤

(0.001) (0.001)

⇤p < 0.10, ⇤⇤

p < 0.05, ⇤⇤⇤p < 0.01

Marginal e↵ects are interpreted as changes to the probability that a group

achieves the socially-optimal enrollment configuration in the average round.

Headings represent a conditionality on enrollment pattern and information.

Background

MotivatingCase Study

Model

Results

Conclusions

Treatments 1 & 2: Budgetary information reduces likelihood of achieving

socially-optimal pattern(for contiguous treatment).

Marginal e↵ects

Contiguous Fragmented

Providing -0.060⇤⇤ -0.005Full Info. (0.025) (0.018)

Full Info. No Info.

Switching to a -0.031 0.024Cont. Scenario (0.024) (0.019)

⇤p < 0.10, ⇤⇤

p < 0.05, ⇤⇤⇤p < 0.01

Marginal e↵ects are interpreted as changes to the probability that a group

achieves the socially-optimal enrollment configuration in the average round. The

headings in the first column show the marginal e↵ects being considered while the

headings in columns 2 and 3 represent a conditionality on enrollment pattern and

information.

Background

MotivatingCase Study

Model

Results

Conclusions

Treatments 1 & 2: Budgetary information reduces player rents by delaying

attainment of socially-optimal pattern (join Messer et al. 2017).

Background

MotivatingCase Study

Model

Results

Conclusions

Conclusions

• Adaptive and mitigative investments into species recoveryshould be jointly determined.

• Provide a framework where the costs of management infragmented areas mediates the budget for landownerincentive programs.

• Similar to Messer et al. (2017), mechanism breaks downwhen severe heterogeneity exists in private costs.

• Similar to Palm-Forster et al. (2016), low uptake rendersmitigative investments cost-ine↵ective.

• Address both of these problems by automated reallocationof budget to adaptive management options when alandowner incentive program is ailing.

Background

MotivatingCase Study

Model

Results

Conclusions

Thank you!

Background

MotivatingCase Study

Model

Results

Conclusions

Regression model

Prob(Si ,t,c,k = 1) =

�

✓↵0 + �1t + �2�c=1 + �3(t · �c=1) + �4�

k=K

+

�5(t · �k=K

) + �6(�k=K

· �c=1) + "

i ,t,c,k

◆

•i is the index of subjects

•t is the number of period

• � and � are binary variables

– c = 1 for parameter treatment “Continuous”– k = 1 for information treatment “Known”