¿De dónde viene y para dónde va la deforestación en Colombia?

Liliana M. Dávalos

ICESI17 Julio 2017

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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A reanalysis of the data Unpublished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Unpublished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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Rapid forest loss in Guaviare Dávalos et al. 2014

Biol. Cons.

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The hamburger connection

• Cattle increase ✔• Demand beef ✘• Revenue beef ✘

Dávalos et al. 2014 Biol. Cons.

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The coca connection

• Cultivation increase ✘• Effect of

eradication?

Dávalos et al. 2014 Biol. Cons.

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Urban development eliminates coca

• More urban, less coca• At ~50% urban

population• No coca in smaller

municipalities

Dávalos et al. 2014 Biol. Cons.

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El RetornoSan Jose

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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!