¿De dónde viene y para dónde va la deforestación en Colombia?
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Transcript of ¿De dónde viene y para dónde va la deforestación en Colombia?
¿De dónde viene y para dónde va la deforestación en Colombia?
Liliana M. Dávalos
ICESI17 Julio 2017
!
What we do in the lab The Dávalos Lab
A double mission
Biological diversityEvolution Extinctionincrease decrease
Come to ColEvol! 17 August 2017
Biological diversityEvolution Extinctionincrease decrease
Focus on deforestation A recent headline
Biological diversityEvolution Habitat lossincrease decrease
A positive exponential trend Álvarez 2002 Cons. Biol.
Dávalos et al. 2011 Env. Sci. & Tech.
Coca = deforestation
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.
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
Rates higher in areas without coca, but Bolivia Data from Hansen et al.
2013 Science
Direct loss rate from coca is low UNODC 2016 World
Drug Report
In Bolivia rates did rise with coca boom Killeen et al. 2007
Ambio
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.
Not in Amazonia, but still signal Armenteras et al. 2013
Reg. Environ. Change
However, generalized linear models cannot be trusted Mets et al. 2017
Ecosphere
A reanalysis of the data Unpublished
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
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Unpublished
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
• MiningDávalos 2001 Biod. & Cons.
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
Modeling forest loss in San Lucas
• Time• 2002-2007• 2007-2010
• Factors• Roads• Rivers• Proximity to other
crops
Chadid et al. 2015 Forests
Modeling forest loss in San Lucas
• Time• 2002-2007• 2007-2010
• Factors• Roads• Rivers• Proximity to other
crops
Chadid et al. 2015 Forests
Pastures and coca behave differently Chadid et al. 2015
Forests
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 importantChadid et al. 2015 Forests
Most forest loss = pasture = cattle? Kaimowitz et al. 2004
CIFOR
Guaviare forests, coca, pastures and cattle
Hamburger! (or steak)Kaimowitz et al. 2004 CIFOR
CocaDávalos et al. 2011 Environ
Sci Technol
Land tenure and propertyHecht 1993 BioScience
Three hypotheses, three sets of predictions
Hamburger! (or steak)Kaimowitz et al. 2004 CIFOR
CocaDávalos et al. 2011 Environ
Sci Technol
Land tenure and propertyHecht 1993 BioScience
+ demand beef + beef, + cattle + cattle, + pasture + pasture, - forest
+ demand cocaine + cocaine, + coca + coca, - forest
+ demand land + pasture, + cattle + cattle, - forest
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Figure 2
BA
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2001 2004 2007 201025
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Rapid forest loss in Guaviare Dávalos et al. 2014
Biol. Cons.
A
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Figure 4
Calamar
El Retorno
San Jose
30,000
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90,000
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of b
eef (
peso
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The hamburger connection
• Cattle increase ✔• Demand beef ✘• Revenue beef ✘
Dávalos et al. 2014 Biol. Cons.
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0 3000 6000 9000Eradication previous year (ha)
Coc
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mpio●
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CalamarRetornoSan Jose
The coca connection
• Cultivation increase ✘• Effect of
eradication?
Dávalos et al. 2014 Biol. Cons.
Municipality●
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CalamarEl RetornoSan Jose
Figure 6
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Percentage population urban
Coc
a cu
ltiva
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(ha)
Urban development eliminates coca
• More urban, less coca• At ~50% urban
population• No coca in smaller
municipalities
Dávalos et al. 2014 Biol. Cons.
A
B
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Figure 5
Calamar
El RetornoSan Jose
2010
0.00
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2000 2002 2004 2006 2008Year
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l GD
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DP
(109
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s)P
rope
rty T
ax(1
06 pe
sos/
capi
ta)Urban/developing
Guaviare
• Larger tax base• More construction
GDP• Finance more
important• Less dependence on
ranching (and agriculture)
Dávalos et al. 2014 Biol. Cons.
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.
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
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
Using MODIS to forecast loss
• Focus on Guaviare• Loss ~ distance to fires• Spatial autocorrelation• Bayesian spatial modeling
Armenteras et al. 2017 Ecol. Appl.
Predictions and loss Armenteras et al. 2017 Ecol. Appl.
Example 2013 Armenteras et al. 2017 Ecol. Appl.
Model Alertas
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.
¿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
¿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
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.
¿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
Thanks!