Presentation by Enrique de la Montaña at the symposium, "Innovative ways for conserving the ecosystem services provided by bushmeat" in the 51th Annual Meeting ATBC 2014 in Cairns, Australia.
Insights from a bioeconomic model of hunting behavior
1. Predicting hunting behavior among
indigenous communities in Ecuador:
insights from a bioeconomic model
Enrique de la Montaña
Eloy Alfaro University (Manta, Ecuador)
2. 0
50
100
150
200
250
0 200 400 600 800 1000
Bushmeat hunting in Ecuador
Sources: de la Montaña 2013; Fa and Peres 2001
Number of consumers
Percapitamammalsbiomasssharvest
kg/person/year
3. TRADICIONALLY EMPIRICAL
Economic approaches in the region
OUR AIM
To develop a bioeconomic model of hunter’s
behavior to analyze the impact of key economic
parameters on bushmeat hunting
- Wildlife consumption
- Hunting
- Game abundance
- Bushmeat demand
- Income
- Price
- Wealth
5. 6 months of surveys
Indigenous field assistant
II. Socioeconomic weekly survey
III. Hunter’s survey
I. Hunting and fishing daily survey
Methodology: three structured surveys
Household sample 55 out of 75:
OUTSIDE
RESERVE
RESERVE
BORDER
INSIDE
RESERVE
29/42 10/11 16/22
7. Survey Results: Income received
38,780,290,5
OUTSIDE INSIDEBORDER
Total income per week/household (US$)
Distribution of income per household SOURCES
LABOURER
AGRICULTURAL
FORESTRY
HUNTING
FISHING
ARTISTRY
OUTSIDE INSIDEBORDER
9. Dynamic model of hunter’s behaviour (see Damania et al. 2005)
Household utility is represented by a Cobb-Douglas function:
-Three productive activities:
Bushmeat hunting
Fishing
Off-farm activities
-All species are considered together like only one species
= household consumption of goods
γ= proportion of bushmeat consumed
= biomass of the animals hunted
= proportion of fish consumed
= biomass of the fish caught
The Model
α α γ α
10. w = wage
Loff = labor time dedicated to off-farm work
Pr = price of good
Ch and Cy = unit cost of hunting and fishing inputs
θ = probability that the hunter will be caught selling bushmeat
K = fine
I. Budgetary constraint:
Lh = labor time dedicated to hunting
N = biomass of the game species (stock)
A = hunting area
g = group size of the species
and = technical parameter
The Model: Constraints
II. Hunting production function:
11. IV. Labor constraint:
III. Fishing production function:
= effect of fish stock on capture
Ly = labor time dedicated to fishing
δ = productivity of the labor force dedicated to fishing
Loff = labor dedicated to off-farm work
Lh = labor dedicated to hunting
Ly = labor dedicated to fishing
The Model: Constraints
15. Simulation results: Wages off-farm
0
5
10
15
20
OUTSIDE BORDER INSIDE
Before After
+25% INCREASE wages +50% INCREASE wages
-32%-32%
0
5
10
15
20
OUTSIDE BORDER INSIDE
Before After
-51%
-51%-51%
-33%
Huntingtime(hour/week/household)
16. Simulation results: Penalty
0
5
10
15
20
OUTSIDE BORDER INSIDE
Before After
0
5
10
15
20
OUTSIDE BORDER INSIDE
Before After
FINE= US$11,4
Confiscation = US$4
Probability of detection = 20%
-100%-99,8%
-50%
-59%-52%
-99,1%
Huntingtime(hour/week/household)
NO FINE
Confiscation = US$4
Probability of detection = 20%
17. Conclusions
Rising bushmeat prices increase time dedicated to hunting,
which will likely lead to declines in game and thereby threaten the
well-being of the indigenous population.
Conversely, declining bushmeat prices improve wildlife
conservation and cultural survival.
Hunting costs is the parameter with the least impact in time
dedicated to hunting.
Increased wages lead to a proportional reduction in time
dedicated to hunting.
A robust system of rules and enforcement represents the best
strategy for regulating hunting activity and controlling illegal
trade in bushmeat.