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Predicting hunting behavior among
indigenous communities in Ecuador:
insights from a bioeconomic model
Enrique de la Montaña
Eloy Alfaro University (Manta, Ecuador)
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
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
COLOMBIA
PERÚ
CUYABENO
RESERVE
Study area
WAJOSARA
DURENO
CUYABENO
ECUADOR
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
Hunted individuals 837
Hunted biomass (pounds) 12382
Fished biomass (pounds) 3749
- Biomass harvest per week/household
Survey Results: Wildlife harvest
- Total harvest in 6 months
Pounds
0
2
4
6
8
10
12
14
16
OUTSIDE BORDER INSIDE
BUSHMEAT
FISH
Survey Results: Income received
38,780,290,5
OUTSIDE INSIDEBORDER
Total income per week/household (US$)
Distribution of income per household SOURCES
LABOURER
AGRICULTURAL
FORESTRY
HUNTING
FISHING
ARTISTRY
OUTSIDE INSIDEBORDER
Survey Results: Expenditure
0
1
2
3
4
5
US$/week BUSHMEAT
BEEF
PIG
FISH
TUNA
EGG
CHICKEN
Household expenditure in protein per week
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 
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:
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
Simulation results: Bushmeat prices
0
5
10
15
20
25
30
OUTSIDE BORDER INSIDE
Benchmark Price variation
0
5
10
15
20
25
30
OUTSIDE BORDER INSIDE
Benchmark Price variation
+25% INCREASE prices +50% INCREASE prices
Huntingtime(hour/week/household)
OVERHUNTING AND DECREASE IN WELL-BEING
+52%
+110%
+131% +115%
+50%
+60%
Simulation results: Bushmeat prices
0
5
10
15
20
25
30
OUTSIDE BORDER INSIDE
Benchmark Price variation
0
5
10
15
20
25
30
OUTSIDE BORDER INSIDE
Benchmark Price variation
-42%-47%
-73%
-74%-80%
-41%
-25% DECREASE prices -50% DECREASE prices
Huntingtime(hour/week/household)
Simulation results: Hunting costs
+50% INCREASE costs
0
5
10
15
20
OUTSIDE BORDER INSIDE
Before After
-8%-19%
-5%
Huntingtime(hour/week/household)
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)
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%
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.
THANKS
enriquedelamontana@gmail.com
We are very grateful to indigenous people of Dureno, Wajosara and Cuyabeno, and
the next institutions:

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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
  • 6. Hunted individuals 837 Hunted biomass (pounds) 12382 Fished biomass (pounds) 3749 - Biomass harvest per week/household Survey Results: Wildlife harvest - Total harvest in 6 months Pounds 0 2 4 6 8 10 12 14 16 OUTSIDE BORDER INSIDE BUSHMEAT FISH
  • 7. Survey Results: Income received 38,780,290,5 OUTSIDE INSIDEBORDER Total income per week/household (US$) Distribution of income per household SOURCES LABOURER AGRICULTURAL FORESTRY HUNTING FISHING ARTISTRY OUTSIDE INSIDEBORDER
  • 8. Survey Results: Expenditure 0 1 2 3 4 5 US$/week BUSHMEAT BEEF PIG FISH TUNA EGG CHICKEN Household expenditure in protein per week
  • 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
  • 12. Simulation results: Bushmeat prices 0 5 10 15 20 25 30 OUTSIDE BORDER INSIDE Benchmark Price variation 0 5 10 15 20 25 30 OUTSIDE BORDER INSIDE Benchmark Price variation +25% INCREASE prices +50% INCREASE prices Huntingtime(hour/week/household) OVERHUNTING AND DECREASE IN WELL-BEING +52% +110% +131% +115% +50% +60%
  • 13. Simulation results: Bushmeat prices 0 5 10 15 20 25 30 OUTSIDE BORDER INSIDE Benchmark Price variation 0 5 10 15 20 25 30 OUTSIDE BORDER INSIDE Benchmark Price variation -42%-47% -73% -74%-80% -41% -25% DECREASE prices -50% DECREASE prices Huntingtime(hour/week/household)
  • 14. Simulation results: Hunting costs +50% INCREASE costs 0 5 10 15 20 OUTSIDE BORDER INSIDE Before After -8%-19% -5% Huntingtime(hour/week/household)
  • 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.
  • 18. THANKS enriquedelamontana@gmail.com We are very grateful to indigenous people of Dureno, Wajosara and Cuyabeno, and the next institutions: