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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”