This document summarizes research on deforestation trends in Colombia. It finds that while coca cultivation has contributed to deforestation, cattle ranching for beef production is a larger underlying driver. Deforestation occurs through a process of frontier expansion, with coca sometimes opening up new areas that are later converted to pasture. The rate of deforestation is now accelerating in the Guaviare region and being driven by urbanization along new road networks. Early detection of fires using MODIS satellite data allows probabilistic modeling of deforestation risks that can help target conservation efforts.

1. ¿De dónde viene y para dónde va la deforestación
en Colombia?
Liliana M. Dávalos
ICESI
17 Julio 2017
!

2. What we do in the lab
The Dávalos Lab

3. A double mission
Biological
diversity
Evolution Extinctionincrease decrease

4. Come to ColEvol! 17 August 2017
Biological
diversity
Evolution Extinctionincrease decrease

5. Focus on deforestation A recent headline
Biological
diversity
Evolution Habitat lossincrease decrease

6. A positive exponential trend
Álvarez 2002 Cons. Biol.

7. Dávalos et al. 2011 Env.
Sci. & Tech.
Coca = deforestation

8. Evaluating effects of
coca
• Direct vs. indirect
• Plots small, direct
effect small
• Indirect effects larger
• There are other countries
• Should hold across
producers
• Background
deforestation ≠ 0
• Must control for other
factors
• E.g., roads, population
Dávalos et al. 2011 Env.
Sci. & Tech.

9. If coca cultivation is an
important factor then:
• Direct effects
• Loss rate high
• Compared to other
agriculture
• Indirect effects:
• Loss rate higher in
producer countries/
areas
• Times with more coca
correspond to more
deforestation
• Coca cultivation will
covary with rates

10. Rates higher in areas
without coca, but Bolivia
Data from Hansen et al.
2013 Science

11. Direct loss rate from
coca is low
UNODC 2016 World
Drug Report

12. In Bolivia rates did rise
with coca boom
Killeen et al. 2007
Ambio

13. The best data are
from Colombia
• Three ≠ studies
• Dávalos et al. 2011
Env. Sci. & Tech.
• LandSat 2002-2007
• Armenteras et al. 2013
Reg. Environ. Change
• LandSat 1990-2005
• Sánchez Cuervo & Aide
2013 Ecosystems
• MODIS 2001-2010
Dávalos et al. 2011 Env.
Sci. & Tech.

14. Not in Amazonia, but still
signal
Armenteras et al. 2013
Reg. Environ. Change

15. However, generalized linear
models cannot be trusted
Mets et al. 2017
Ecosphere

16. A reanalysis of the data Unpublished

17. Key points, illicit crops
• Direct effects
• Loss rate high ✘ when
compared to other
agriculture
• Indirect effects:
• Loss rate higher in
producer countries ✘
• Times with more coca
correspond to more
deforestation ✔ in Bolivia
• Coca cultivation will covary
with rates ✘
Novoa & Finer 2015

18. 200 km
40 km
Not all effects
depend on rates
• If endemic species
• Small area = large
effect
• Biodiversity in Andes,
Chocó
• High
• Irreplaceable
• Detailed and focused
analyses needed
Unpublished

19. Serranía de San
Lucas
• Last large remnant of
Andean forest in
Colombia
• Unprotected
• Almost declared a park
in 2010
• Decision postponed
because of gold mining
• Threats:
• Agriculture including
coca
• Mining
Dávalos 2001 Biod. &
Cons.

20. Dynamics of San Lucas
stand out
Mets et al. 2017
Ecosphere
San Lucas
Santa Marta
San Lucas
Santa Marta
San Lucas
Santa Marta
San Lucas
Santa Marta

21. Modeling forest loss
in San Lucas
• Time
• 2002-2007
• 2007-2010
• Factors
• Roads
• Rivers
• Proximity to other
crops
Chadid et al. 2015
Forests

22. Modeling forest loss
in San Lucas
• Time
• 2002-2007
• 2007-2010
• Factors
• Roads
• Rivers
• Proximity to other
crops
Chadid et al. 2015
Forests

23. Pastures and coca
behave differently
Chadid et al. 2015
Forests

24. Key points, San
Lucas
• Deforestation accelerating
• Focus on annual
deforestation obscures
pattern
• Direct loss
• Coca << pasture
• But coca not negligible
• Operates as spearhead
for later cultivation
• Frontier dynamics
• Roads either not recorded or
not important
Chadid et al. 2015
Forests

25. Most forest loss =
pasture = cattle?
Kaimowitz et al. 2004
CIFOR

26. Guaviare forests, coca, pastures and cattle
Hamburger! (or steak)
Kaimowitz et al. 2004 CIFOR
Coca
Dávalos et al. 2011 Environ
Sci Technol
Land tenure and property
Hecht 1993 BioScience

27. Three hypotheses, three sets of predictions
Hamburger! (or steak)
Kaimowitz et al. 2004 CIFOR
Coca
Dávalos et al. 2011 Environ
Sci Technol
Land tenure and property
Hecht 1993 BioScience
+ demand beef
+ beef, + cattle
+ cattle, + pasture
+ pasture, - forest
+ demand cocaine
+ cocaine, + coca
+ coca, - forest
+ demand land
+ pasture, + cattle
+ cattle, - forest

28. 1400
1600
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3035404550
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NP
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Pasture
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0.51.01.52.02.5
Coca
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PA(ha)
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PLAND
Figure 2
BA
G
C
E FD
H I
25
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40
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2001 2004 2007 2010
25
30
35
40
2001 2004 2007 2010
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2001 2004 2007 2010
PLAND
K L M
Rapid forest loss in
Guaviare
Dávalos et al. 2014
Biol. Cons.

29. A
B
C
Figure
Calamar
El Retorno
San Jose
30,000
60,000
90,000
10
20
30
Year
CattlePriceofbeef(pesos/Kg)RanchingGDP(109
pesos)
2000 2002 2004 2006 2008 2010
1,600
1,800
2,000
2,200
The hamburger
connection
• Cattle increase ✔
• Demand beef ✘
• Revenue beef ✘
Dávalos et al. 2014
Biol. Cons.

30. ●
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2000
4000
6000
0 3000 6000 9000
Eradication previous year (ha)
Cocacultivation(ha)
The coca connection
• Cultivation increase ✘
• Effect of
eradication?
Dávalos et al. 2014
Biol. Cons.

31. Municipality
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Calamar
El Retorno
San Jose
Figure 6
A B
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30,000 60,000 90,000
Cattle
Percentagelandpasture
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2,000
4,000
6,000
30 40 50 60
Percentage population urban
Cocacultivation(ha)
Urban development
eliminates coca
• More urban, less coca
• At ~50% urban
population
• No coca in smaller
municipalities
Dávalos et al. 2014
Biol. Cons.

32. A
B
C
Figure 5
Calamar
El Retorno
San Jose
2010
0.00
0.02
0.04
0.06
20
30
40
50
2
3
4
5
2000 2002 2004 2006 2008
Year
FinancialGDP
(109pesos)
ConstructionGDP
(109pesos)
PropertyTax
(106pesos/capita)
Urban/developing
Guaviare
• Larger tax base
• More construction
GDP
• Finance more
important
• Less dependence on
ranching (and
agriculture)
Dávalos et al. 2014
Biol. Cons.

33. Key points, Guaviare
• Rapidly urbanizing
• Catalysts:
• Bogotá-Villavicencio road time
cut in half since 1990s
• Improving road Villavicencio-
San José
• Expectation of urbanization
• Land grabs
• Technology
• Especially farther into Llanos
• Ley 2 1959 (national forest
reserve)
• Ineffective
Dávalos et al. 2014
Biol. Cons.

34. Previous analyses
• Pixels high resolution
• LandSat = 30 m
• Frequency annual
• Or lower depending on
cloud cover
• Forests frontiers are cloudy
places
• Annual is too late
Whitehead 2016
Google Earth Blog

35. One alternative
• Pixels low resolution
• MODIS = 250 m
• Frequency ~1.5 days
• Lower for tropics
• On average ~15 days
• Especially useful for
detecting fires
• Already used in Brazil
Schmaltz 2003 Fires in
Venezuela and Colombia

36. Using MODIS to
forecast loss
• Focus on Guaviare
• Loss ~ distance to fires
• Spatial autocorrelation
• Bayesian spatial modeling
Armenteras et al. 2017
Ecol. Appl.

37. Predictions and loss Armenteras et al. 2017
Ecol. Appl.

38. Example 2013 Armenteras et al. 2017
Ecol. Appl.
Model Alertas

39. Key points,
prediction
• Deforestation follows frontier
dynamics
• With few exceptions
• Annual and after the fact is
too late
• Need self-updating tools
• Edges vulnerable, main tool
is fire
• Probabilistic model can
update Alertas system
Armenteras et al. 2017
Ecol. Appl.

40. ¿De dónde viene?
• Coca has been blamed for a
lot of deforestation
• But many activities
involved
• Most forest ends up as
pasture
• Most important incentives
have to do with land as
value
• Deforestation is about land
as a resource

41. ¿De dónde viene?
• But coca is an important
indicator
• Opens up beachheads in
many areas
• Effects devastating where
biodiversity high
• Andean forests
• Chocó
• Size ≠ effect in high-
biodiversity regions

42. Future = past?
• Closing of the forest frontier
• Forest->property
• End state = no forest
• Already happened in other
regions
• E.g., parts of Caquetá,
Putumayo (Mocoa),
central Andes
• Currently unfolding in
Amazonia parts of Chocó
Etter et al. 2006 J.
Environ. Manage.

43. ¿Para dónde va?
• Wherever development goes
• Roads
• Population (migration)
• Fires
• Strong indicator of ongoing
and future activities
• Can be used for
monitoring, need action
though

44. Thanks!