Forests and Drugs: Coca-Driven Deforestation in Tropical Biodiversity Hotspots

Published: January 11, 2011

r 2011 American Chemical Society 1219 dx.doi.org/10.1021/es102373d | Environ. Sci. Technol. 2011, 45, 1219–1227

ARTICLE

pubs.acs.org/est

Forests and Drugs: Coca-Driven Deforestation in TropicalBiodiversity HotspotsLiliana M. D�avalos*

Department of Ecology and Evolution and Consortium for Inter-Disciplinary Environmental Research, SUNY Stony Brook,650 Life Sciences Building, Stony Brook, New York 11794-5245, United States

Adriana C. Bejarano

Department of Environmental Health Sciences, Public Health Research Center 401, University of South Carolina, 921 Assembly Street,Columbia, South Carolina 29208, United States

Mark A. Hall

Department of Ecology and Evolution, SUNY Stony Brook, 650 Life Sciences Building, Stony Brook, New York 11794-5245,United States

H. Leonardo Correa

Sistema Integrado de Monitoreo de Cultivos Ilícitos, United Nations Office on Drugs and Crime, Calle 102 no. 17A-61,Bogot�a, Colombia

Angelique Corthals

Department of Sciences, John Jay College of Criminal Justice (CUNY), 899 Tenth Avenue, New York, New York 10019,United States

Oscar J. Espejo

Sistema Integrado de Monitoreo de Cultivos Ilícitos, United Nations Office on Drugs and Crime, Calle 102 no. 17A-61,Bogot�a, Colombia

bS Supporting Information

ABSTRACT: Identifying drivers of deforestation in tropical biodiversity hotspots is critical to assess threats to particular ecosystemsand species and proactively plan for conservation. We analyzed land cover change between 2002 and 2007 in the northern Andes,Choc�o, and Amazon forests of Colombia, the largest producer of coca leaf for the global cocaine market, to quantify the impact ofthis illicit crop on forest dynamics, evaluate the effectiveness of protected areas in this context, and determine the effects oferadication on deforestation. Landscape-level analyses of forest conversion revealed that proximity to new coca plots and a greaterproportion of an area planted with coca increased the probability of forest loss in southern Colombia, even after accounting for othercovariates and spatial autocorrelation. We also showed that protected areas successfully reduced forest conversion in coca-growingregions. Neither eradication nor coca cultivation predicted deforestation rates across municipalities. Instead, the presence of newcoca cultivation was an indicator of municipalities, where increasing population led to higher deforestation rates. We hypothesizethat poor rural development underlies the relationship between population density and deforestation in coca-growing areas.Conservation in Colombia’s vast forest frontier, which overlaps with its coca frontier, requires a mix of protected areas and strategicrural development to succeed.

’ INTRODUCTION

A substantial portion of the world’s biodiversity is located inhotspots, regions harboring a disproportionate number of ende-mic species.1 These hotspots predominantly encompass extremelybiodiverse and increasingly threatened tropical forest ecosystems.2

The high concentration of unique species in tropical forest hot-spots increases the odds that local disturbances translate into

global extinctions. Quantifying forest loss in the hotspots istherefore critical to plan for conservation at all spatial scales.3

Received: July 13, 2010Accepted: December 20, 2010Revised: December 15, 2010

1220 dx.doi.org/10.1021/es102373d |Environ. Sci. Technol. 2011, 45, 1219–1227

Environmental Science & Technology ARTICLE

But documenting deforestation patterns is not enough: identify-ing local and regional drivers of forest loss is indispensable toaddress the causes of biodiversity loss, and plan for conservationin a proactive manner.4

Although it is hard to synthesize global deforestation patternsand uncover what drives these processes,5 agricultural markets,resource booms, and roads have been closely linked to increasesin deforestation.6 These markets and the roads built to supplythem are, in turn, linked to changes in policy regimes anddemand for agricultural products, fossil fuels, or other naturalresources.7,8 At the same time, a critical question for implement-ing biodiversity conservation in the tropics is whether and howconservation policy affects deforestation.9 Because the effects ofmarkets, as well as development and conservation policies arespecific to the environmental and socioeconomic context of eachcountry, region, and location,10,11 analyses of their impact onbiodiversity hotspots can provide the level of detail necessary totake action and reduce immediate drivers of deforestation.

In this paper, we analyze forest cover over the past decade toelucidate local and regional drivers of forest loss in Colombia.Wefocus on Colombia because it encompasses two of the mostdistinctive global biodiversity hotspots, the tropical Andes andthe Choc�o,1 and ∼12% of the Amazon forest.12 Over the last 20years the pace of deforestation in Colombia has accelerated,8,13,14

particularly in lowland forests,15,16 even as demographic pres-sures have eased and the proportion of the population dependenton agriculture has declined.17 Because this period largely overlapswith the explosion of coca cultivation for the cocaine market,when Colombia went from growing 10% of the global cocaproduction in 1987 (220 km2) to 74% in 2000 (1633 km2),18

several analyses have suggested coca cultivation directly andindirectly drives deforestation in Colombia’s forestedfrontier.15,19,20 Eradication by aerial spraying of herbicide is themain and most widespread institutional response to the expan-sion of coca cultivation.21 The eradication program itself couldcontribute to deforestation by facilitating the degradation offorest remnants,22 pushing growers out of targeted areas to newlands making colonization and deforestation more dynamic.18,23

The ultimate driver of coca cultivation and the governmentprograms that attempt to suppress it is the global demand forcocaine.18 However, reliable data on temporal variation of globaldemand are lacking24 and even order of magnitude estimates ofthe cash flow in this market are unreliable.25 Therefore, ouranalyses focus on the supply for this global commodity market, alikely proximate driver of deforestation.

Even as the pace of forest loss in Colombia has accelerated, thecountry has consolidated its biodiversity conservation policyaround a large network of protected areas.26 The effectivenessof Colombian protected areas against deforestation has beenestablished,27 particularly in lowland Amazonian forests,19,28 buttheir ability to prevent or mitigate deforestation spurred by illicitcrops remains to be demonstrated. We analyzed forest coverdata for the 2002-2007 period in Colombia with threeobjectives: (1) to determine the indirect effects of coca cultiva-tion on deforestation, (2) to evaluate the effectiveness ofprotected areas in avoiding deforestation in coca-growingregions, and (3) to examine the role of coca eradication indeforestation. We used landscape analyses to investigate thefirst two objectives, and analyzed municipality deforestationrates to examine the third objective. Our ultimate goal was tohelp guide biodiversity policy and management by uncoveringthe impact of illicit crops and their eradication on deforestation

and measuring the effect of current conservation policies in thiscontext.

’EXPERIMENTAL SECTION

Remote Sensing and Land Cover. We used land cover mapsgenerated to detect coca cultivation in 2002 and 2007 to quantifyforest cover dynamics in Colombia. The illicit crop monitoringsystem of Colombia (SIMCI), used Landsat 7 Enhanced ThematicMapper Plus (ETMþ), supplemented with Aster, SPOT, andIRS-LISS III images to assemble the 2002 and 2007 land covermaps. The images were preprocessed over SIMCI’s planimetricgeoreferencing layers for Colombia, and modified to removetopographic and terrain distortions.29 To standardize the digitaland visual interpretation, the visual images were corrected fortopography using ERS radar images at 20-m resolution. Landcover data were extracted from the images through visual inter-pretation and supervised multispectral classification using PCIGeomatics software.To validate the classification of these land cover maps,

particularly as it applied to coca cultivation, SIMCI conductedhelicopter or small aircraft reconnaissance flights approximatelyevery 10 km in 4 types of areas: (1)∼75% of the areas where cocahas been grown historically, (2) sites where authorities havereported new coca cultivation, (3) areas where eradication byaerial fumigation has been reported, and (4) areas with densecloud cover in remote sensing images. Georeferenced photo-graphs collected from these surveillance flights were then usedto calibrate and validate land cover classification, and in particularto verify the presence of coca in a given area (as opposed to othershrubby crops). Coca grown in the shade cannot be detectedusing these surveys.On-the-ground truthing of remote sensing data was also

conducted, but was more limited in scope, targeting only areasof new cultivation or specific survey sites of particular social30 orecosystem importance.31 The aerial and on-the ground observa-tions were then used to improve classification of remotely senseddata. Clouds, shadows and gaps in the remote sensing imageswere classified as missing data and excluded from all analyses forboth years.Land cover maps generated this way divided the country

into three regions: North, Central, and South, encompassing>450,000 km2, and the entire range of natural forests in thenorthern Andes and Choc�o biodiversity hotspots 1 and 34 pro-tected areas (Figure 2a). On the basis of these maps, we com-pared land cover in this time step using IDRISI Andes software.32

Complete forest regeneration is not expected to occur during the5-year span of our analysis, can only occur from fallows tosecondary forest. To test the accuracy of the land cover classifica-tion, we checked the transitions for regeneration from clearedareas to primary forest (<0.03% of pixels in each region), andthese were excluded from subsequent analyses. No transitionsfromwater to forest were recorded, corroborating the accuracy ofmost of the classification.Modeling Forest Cover Change and Deforestation Rates.

Tomeasure the effect of coca cultivation and conservation policyon forest cover, we modeled change in forest cover as a functionof a series of environmental and policy variables, includingdistance to the closest coca field and the area of coca cultiva-tion in a 1-km2 cell, protection status of the landscape cell, relief,accessibility, climate, and remaining forest cover (Table 1,Figure 1). To summarize 19 biologically important climate



variables comprising temperature and precipitation means,extremes, and seasonality, 33 we obtained principal components(PC) by eigenvalue decomposition of the climate rasters inArcGIS v. 9.2.34 We used these PCs rather than the climate dataas predictors in subsequent analyses to minimize collinearityamong variables. PC1 comprised mostly precipitation and itsseasonality, PC2 reflected mostly temperature and its varia-tion, and PC3 comprised the remainder of the variation in thevariables.We used the Akaike Information Criterion (AIC) to select

the best combination of predictors of forest cover change foreach region.35 Logistic regressions of change in forest cover as afunction of the predictors were fitted using both a generalizedlinear model (GLM) approach assuming independence in errors,and a generalized estimating equation approach (GEE), whichincluded an autocorrelation structure for the errors of observa-tions within a 5 � 5 km box.36 The last approach was used toaccount for spatial autocorrelation in model fitting so thathypothesis testing was not biased toward rejection of the nullhypothesis by virtue of the similarity of errors among observa-tions close to one another.All models were calibrated using a small subset of the observed

changes, and validated using all the data 37 (Table S1). Theaccuracy of the models was assessed using the area under thereceiver operator characteristic curve or AUC, which ranges from0 to 1, with 0.5 indicating a completely random model, and 1indicating a perfect model.38

To measure the effect of eradication by aerial spraying(Figure 1F) we calculated annual municipal deforestation ratesusing eq 7 from ref 39. Deforestation rates were modeled as afunction of changes in the density of human population, cocacultivation, and eradication using multilevel linear models withdistance-based autocorrelation structures.40 Again, autocorrela-tion structures were included to remove bias in hypothesis testing

arising from spatial autocorrelation. Fitting separate intercepts orslopes for different groups of municipalities would have a similareffect by accounting for unobserved variation through a randomeffect of group assignments. Models were compared using theAIC.35

All modeling steps were conducted in the R statistical language 41

using the MASS library,42 and the ncf,43 geepack,44 ROCR,45 andnlme 46 packages. Details on the Experimental Section are presentedin the Supporting Information.

’RESULTS

Land Cover Change between 2002 and 2007. The propor-tion of land cover classes and relative change are summarized inTable 2. Natural vegetation, forest and scrub, was the mostprevalent land cover class, and most of it remained unchangedthrough 2007. Despite the general stability in land cover classes,the Central region experienced dramatic losses in forest coverfrom 56% to 46% of its surface, and the South went from 82 to78% forested. Substantial forest regeneration was only observedin the South, with 13% of crops and 10% of anthropogenic coverreverting to forest.The highest annual deforestation rate was recorded in the

North (4.70%) followed by the Central region (3.79%), and amuch lower rate of forest loss in the South (0.81%). These annualrates mask large forest losses over the 5-year period: 14,322 km2

lost in the South, a similar area of mostly Andean forests lost inthe Central region (13,630 km2, Figure 2a), and 1,160 km2 lost inthe North. The deforestation rate in the southern Choc�o was0.98%, or 291 km2 of forest lost out of 6100 km2.Modeling Land Cover Change. The samples taken to cali-

brate models were very sparse relative to the data available, <1%of the data in every case (Supporting Information, Table S1),resulting in some variation in the predictors that could be eliminated,

Table 1. Geoferenced Data, Spatial Resolution of Variables, and Data Sources Used in Modeling Land Cover Change andDeforestation Rates

variable type variable description spatial resolution data source

illicit crop and eradication Euclidean distance to new coca fields found

between 2002 and 2006

90-m grid SIMCI

coca cultivation (ha/km2) downscaled to 1-km grid from

30-m grid of coca cultivation

SIMCI

area eradicated by aerial spraying during time step (ha) municipality SIMCI, based on reports filed

by anti narcotics agency

conservation policy IUCN-ranked protected areas (categorical 0/1) 90-m grid ref 65

population population growth (inhabitants/km2) municipality DANE66

accessibility Euclidean distance to nearest primary or secondary road

(including unpaved roads) in km

90-m grid SIMCI

Euclidean distance to nearest navigable river in km 90-m grid SIMCI

climate principal component 1 of pca of 19 climate variables

(precipitation-derived)

1-km grid Worldclim 33

principal component 2 of pca of 19 climate variables

(temperature-derived)

1-km grid Worldclim

principal component 3 of pca of 19 climate variables

(remaining orthogonal climate variation)

1-km grid Worldclim

topography elevation 90-m grid ref 67

aspect 90-m grid processed from ref 67

slope 90-m grid processed from ref 67

remaining natural habitat percent remaining forest cover in 2002 1-km grid current study



as well as the coefficients estimated across series of models(Supporting Information, Tables S2-S8). There was no signifi-cant improvement in model fit from including the coca-relatedvariables in the Central region, or from including the protectedarea variable in the North (Supporting Information, Table S2).Distance to roads and remaining forest cover were importantpredictors in every model, while elevation and aspect were elimi-nated from most models (Supporting Information, Table S2).There was significant spatial autocorrelation in the residuals of

logistic regressions assuming independent observations, indicat-ing that this approach was inadequate for determining thesignificance of predictors. Analyses using GEE reduced, but didnot completely eliminate autocorrelation in the residuals (Figure S3).Increasing the clustering distance up to 100 km did not minimize

the number of significantly autocorrelated residuals, and was notpursued further. Figure 2 and Table 3 show the results from themodel in a series that minimized the proportion of significantlyautocorrelated residuals and was most appropriate for hypothesistesting.Despite variability across samples some predictors were signifi-

cant in every model of a series (Table 3). The probability oftransition from forest to nonforest increased significantly withshorter distance to new coca plots, as well as with the amount ofnew coca per km2 in the South region. Protected areas signifi-cantly decreased the probability of forest loss in the Central andSouth regions, but this effect was not consistent for all models inthe series (Table 3 and Supporting Information Tables S4 and S5).Many of the predictors of forest loss were also predictors of forest

Figure 1. Independent variables included as predictors in analyses of land cover change and deforestation rates. (A) New coca cultivation detectedbetween 2002 and 2007. (B) Relief map and permanently navigable rivers. The rivers layer was used to generate a distance surface and thus estimateaccessibility by river. (C) Primary and secondary road network, including nonseasonal unpaved roads. This network was used to generate a distancesurface and estimate accessibility by road. (D) First principal component (PC) derived from principal component analysis of 19 ecologically importantclimate variables.33 (E) Proportion of remaining forest in 2002 at 1-km2 resolution. (F) Amount of aerial spraying conducted in each municipalitycalculated as the mean annual number of ha sprayed per km2.



Table 2. Transition of Land Cover Classes for the 2002-2007 Perioda

region 2002V/2007f forest crops other natural vegetation anthropogenic % of total area

North forest 0.68 0.23 0.08 0.00 30

crops 0.20 0.68 0.11 0.01 10

other natural vegetation 0.02 0.03 0.94 0.00 58

anthropogenic 0.00 0.07 0.02 0.91 1

% of total area 24 16 59 1

Central forest 0.67 0.26 0.08 0.00 56

crops 0.24 0.68 0.08 0.00 34


anthropogenic 0.04 0.17 0.04 0.75 <1

% of total area 46 39 14 <1

South forest 0.93 0.05 0.02 0.00 82

crops 0.13 0.75 0.11 0.00 15


anthropogenic 0.10 0.36 0.06 0.49 <1

% of total area 78 16 6 <1aThe anthropogenic category included buildings, roads (paved and unpaved), airstrips, (paved and unpaved), and any other anthropogenic land coverclasses excluding cultivation and pastures.

Figure 2. Change in forest cover from 2002 to 2007. (A)Observed change, outline of IUCN-protected areas (national parks, sanctuaries, and biospherereserves), and forested areas discussed in the text: (1) Serranía del Perij�a, (2) Serranía de San Lucas, and (3) Pacific versant of Cordillera Occidental(Choc�o forests). Observed changes were derived from direct comparison of land cover maps. (B)Modeled probability of deforestation based on region-specific landscape models of forest change. (C)Modeled probability of reforestation, note overprediction for this change in the South. The probabilitiesof change as a function of the observations in 2002 were obtained by applying the functions summarized in Table 3 to the predictors in 2002 (Table 1).

Table 3. Coefficients from GEE regression and performance of models with the fewest instances of significant spatialautocorrelation in residuals.a

distance to climate

region coca road river

remaining

forest (%)

coca

(ha/km2) PC1 PC2 PC3 aspect elevation slope

protected

area (binary) AUC

deforestation

North 0.05 -4.45* -0.27* -0.40*** -0.17 -1.09* -0.45* -0.08 -0.03*** 0.83

Central -3.03*** -0.19** -0.24*** -0.43*** 0.54*** -0.08 -0.02*** -0.80** 0.76

South -0.82*** -2.32*** -0.34** -0.60*** 0.16** -0.67*** 0.32** -0.90*** -0.16* -0.26* -0.03*** -0.78** 0.93

reforestation

North -1.98 -1.11*** -0.27 0.92

Central -2.52** -0.62*** -0.23*** -0.11 -0.25*** -0.15** -0.63 0.85

South -0.32 -2.54*** -0.33** -0.71*** 0.13 -0.09 -0.36*** -0.43*** -0.33** -17.36*** 0.96a * Significant at p < 0.05, ** significant at p < 0.01, *** significant at p < 0.001. Coefficients in bold were significant in every model of the series of 10 foreach data set.



gain, reflecting more dynamic landscapes. The performance ofthe models, summarized in Table 3 and Figure 2 ranged from fair(>0.7 AUC) in the Central region to excellent (>0.9 AUC) in theSouth.Modeling Deforestation Rates. Rates of forest loss/gain

varied widely, from -1.13;or 113% annual deforestation in amunicipality in the Central region where ∼0.3% of the originalforest remained after the 5-year period, to >48% annual regen-eration;where the forest grew to 11 times its original size;in amunicipality on the Amazonian versant of the Andes in the South(Supporting Information, Figure S3a). The median municipaldeforestation rate was -0.025 (2.5% annual loss; std. dev. =16.29%). Supporting Information, Figure S3 summarizes defor-estation/reforestation rates by municipality and by 1-km2 pixel.Eradication and coca cultivation were not significant predictors

of deforestation/reforestation rates in any analyses (SupportingInformation, Table S9). Change in population density was asignificant covariate of deforestation rates when assuming anindependent sampling structure, but this effect was lost in thebest models, which included a distance-based error correlationstructure (Supporting Information, Table S9). These nonsigni-ficant relationships between changes in population density anddeforestation rates are summarized in Supporting Information,Figure S4. Assuming independent sampling in municipal analysesresulted in highly autocorrelated residuals, and neither applyingdistance-based correlation structures nor fitting intercepts foreach region reduced residual autocorrelation (Supporting Informa-tion, Figure S5).Separate single-level models with a Gaussian autocorrelation

structure for each region found no significant effect of eithereradication or coca cultivation in any region (P > 0.2793), anda significant effect of change in population density in the South(P = 0.0001), but not in the North or Central region (P > 0.13).The South regional model had minimal residual autocorrelationrelative to both whole-country models and other regional models(Supporting Information, Figure S5). Deforestation rates for theSouth based on the regional model are shown in SupportingInformation, Figure S5b.

’DISCUSSION

In this study we have shown that (1) coca cultivation increasesthe probability of forest conversion in the northern Andes andChoc�o of southern Colombia, (2) changes in population densitypredict deforestation rates in this same region, and (3) protectedareas decrease the probability of forest loss, even after controllingfor accessibility by factoring distances to roads and rivers.How Does Coca Cultivation Promote Deforestation? The

forces driving deforestation result from complex interactionsbetween policy decisions and socioeconomic processes as theyunfold in an environmental space more or less propitious toagriculture and other human activities. Considering the illegalityof coca and local socioeconomic conditions, four nonexclusivemechanisms have been proposed to explain coca as a driver ofdeforestation: (1) armed conflict associated with coca produc-tion and trafficking may drive growers away from existing cropspromoting further deforestation,20 (2) higher income from cocacultivation attracts new growers and drives existing growers toexpand their production,47,48 (3) eradication and law enforcementforce growers to relocate promoting further deforestation,20,49

and (4) eradication may drive deforestation directly.22 Analysesof the role of armed conflict are beyond the scope of this study,

but the demographic and eradication data can help assess the roleof immigration and aerial fumigation in deforestation.Coca cultivation had no effect onmunicipal deforestation rates

(Supporting Information, Table S9), despite the landscape-leveleffects of coca on the probability of conversion (Table 3). Itcould be that the relationship between coca cultivation andprobability of deforestation is captured across the landscape,whereas on the aggregate, migration and many forms of exploita-tion (not measured here) mediate the relationship between cocacultivation and deforestation rates (e.g., ref 48). This explanationimplies that even if coca cultivation attracts immigration, defor-estation rates arise from many other activities and are thereforeonly weakly associated with coca cultivation.If coca attracts new growers who then convert the forest,

population change should be positively linked to coca cultivation,and deforestation rates should be positively related to populationchange in coca-growing areas. We modeled population change asa function of new coca cultivation across municipalities andfound a nonsignificant relationship (P = 0.3222 with all data, P =0.2330 with data from municipalities that recorded new coca).There was some evidence of the link between deforestation ratesand population gains in coca-growing areas, since the onlysignificant relationship was found in the South where most cocais grown (P = 0.0001; Figure 3). To evaluate this relationshipmore broadly we reanalyzed data from municipalities where newcoca was detected (not restricted to the South, N = 227), andfound that deforestation rates increased with growing populationdensity (P = 0.0005). In contrast, municipalities where no newcoca was found (N = 267) showed no significant relation-ship between change in population density and deforestationrate (P = 0.3689). Although we found no evidence that cocacultivation attracts immigrants, our analyses indicate that popu-lation density and deforestation rates are linked in coca-growingmunicipalities. This relationship is not explained by coca cultivationbecause coca was not associated with population density.As with coca cultivation, eradication in coca-growing munici-

palities could translate into higher deforestation rates becauseeradication and law enforcement may result in relocation of cocagrowers and new clearings.18,20,22,49 As law enforcement pressureincreases, eradication would displace people,21 thus explainingdeforestation rates better than coca cultivation.49 However,eradication had no effect on population density in coca-growingmunicipalities (P = 0.7004), or on deforestation rates (SupportingInformation, Table S9).Neither the amount of coca cultivation nor eradication is a

satisfactory explanation for the relationship between population

Figure 3. Deforestation rate as a function of change in populationdensity in the South, fitted line shows relationship (P = 0.0001).



density and deforestation rates in municipalities where coca isexpanding. Previous analyses to establish the characteristics ofregions where coca expands have revealed that (1) the potentialfor expansion based on climate alone coca encompasses virtuallyall lowland forests and some of the subtropical forest remnants inthe country and (2) the prevalence of abject poverty and lowaccessibility sets apart coca growing districts from other agricul-tural areas.50 Both household surveys and analyses above the level ofmunicipalities have shown that socioeconomic conditions are poorand living standards low for most coca growers 18,21,30

In light of previous research, we hypothesize that what setscoca-growing municipalities apart is poor rural development.Gains in rural population density relate to higher deforesta-tion rates because most or all economic activities that absorbimmigrants, or used to occupy emigrants, require forest clearing.Municipalities without new coca would have a diverse suite ofeconomic activities to accommodate population growth, so thatthe relationship between population and deforestation breaksdown. In our data, new coca cultivation and the existing roadnetwork are complementary, suggesting that coca does notexpand in areas with substantial development (cf., Figure 1aand c). The fact that coca, a crop that cannot be grown wheregovernment and law enforcement are active, is expanding inthese municipalities further points to their lack of socio-politicaland economic development. The expansion of coca itself is anindication that these municipalities constitute the agriculturalfrontier, where settled land ends and new inroads begin. If so,these municipalities should have a greater proportion of theirsurface in forest because socio-political integration and economicdevelopment have produced massive forest loss in Colombianhistory.16 We investigated this prediction by modeling boththe presence of new coca and its quantity as functions of theproportion of the municipality that remained in forest in 2002.Both models were highly significant (N = 594, P < 2� 10-16 forthe logistic regression, and P = 0.0011 for the linear model),confirming the expectation that these municipalities are thehitherto undeveloped forested frontier.Our municipal-level analyses suggest that the relationship

between coca cultivation and deforestation is more complex,and the policy context in which landscape-level processes unfoldhas more influence on eventual outcomes 48,49 than previouslyproposed.18,20,22 We propose poor rural economic developmentsignaled by the expansion of coca as the context underlying therelationship between population density and deforestation rates,even in the North and Central region, where coca cultivation wasnot a covariate of the probability of deforestation at the landscapelevel. Analyses of the economic covariates of coca have shownthat proportionally larger rural populations and poverty predomi-nate in coca-growing regions,51 bolstering this interpretation.Coca is expanding in these municipalities because they areunderdeveloped, rather than the converse. Coca is therefore asymptom rather than the ultimate cause of deforestation, andstructural features such as socioeconomic inequality, failed agri-cultural development policies, and armed conflict are the large-scale drivers of deforestation.51-54 More data at levels rangingfrom the household to the municipality are needed to test thehypothesis that the drivers of deforestation in coca growing areasare socioeconomic and linked to underdevelopment and possiblyconflict.Aside from eradication, development, broadly construed to

encompass projects to replace coca with alternative crops,strengthen local institutions, and integrate coca-growing regions

to the national economy through road construction, has beenproposed as a way to curb coca cultivation (see Foreign Opera-tions Congressional Budget requests at ref 55). Our landscape-level analyses, along with several recent studies,8,14,19,28 haveshown that access, particularly by road, is a strong covariate ofdeforestation. Development of coca-growing areas that improvesaccess to the frontier without alleviating the conditions that makeforest conversion the main source of income has the potential togreatly accelerate forest loss in the Central and South regions.The patterns of forest conversion along the Colombia-Ecuadorborder suggest that orderly, development-driven colonizationcan lead to the stabilization of the frontier with a much greaterproportion of remnant forest,14 and that the spontaneouswaves of colonization experienced in southern Colombiastabilize at a much lower proportion of forest.15,56 An alter-native development scenario would be a more orderly coloni-zation process, with roads catalyzing deforestation along theirimmediate vicinity, but larger tracts of forest remaining aseconomic opportunities that do not convert the forest becomeavailable.57

Prospects for Biodiversity Conservation. Our results showthat protected areas reduce the probability of forest conver-sion,19,27,28 not just by virtue of being in remote areas, and even incoca-growing regions. An additional, even larger, effect wouldfollow from conserving a larger proportion of forest (Table 2).Larger forest remnants would, in turn, enhance the prospects forconserving more endemic species than a mosaic where forestremnants are too small and isolated to support many species.58

Expanding the protected area network to conserve large tracts ofthe most biodiverse forest remnants in the three study regions iswarranted considering the high rates of deforestation observedover the past decade in the North and Central region, andsouthern Choc�o, and the historical trajectory toward stabilizationat very low forest cover in midelevations.59,60

Four of eight protected areas slated for official governmentprotection in the 2009/2010 period encompass biodiversityhotspots in the Serranía del Perij�a, Serranía de San Lucas(Figure 2a), dry forests in the easternmost extreme of the mid-Central region, and the Serranía del Pinche in the Choc�o (seehttp://www.parquesnacionales.gov.co/PNN/portel/libreria/php/decide.php?patron=01.1103). On the basis of these immediateexpansion plans, lowland southern Choc�o forests would con-tinue to be underrepresented in the protected area network.Unlike some of the vast parks in Amazonian forests, these newprotected areas would have to be responsive to already-establishedlocal populations and their current economy.61,62 Consideringthat structural underdevelopment would underlie the relation-ship between migration and deforestation rates in Colombia,support for a diverse smallholder economy is crucial to break thecycle of environmental degradation and poverty that traps smallfarmers throughout the agricultural frontier53,63 and simultaneouslyconserve biodiversity beyond strictly protected areas.64 To this end,one of the primary goals of conservation should be to articulate itspriorities within a larger framework seeking to both promote andregulate rural development throughout the forest frontier.

’ASSOCIATED CONTENT

bS Supporting Information. Detailed experimental section,nine tables and five figures of results, and supporting references.This information is available free of charge via the Internet athttp://pubs.acs.org/.



’AUTHOR INFORMATION

Corresponding Author*E-mail: [email protected]; phone:þ1 631 632 1554;fax: þ1 631 632 7626.

’ACKNOWLEDGMENT

We thank Resit Akc-akaya for helpful discussion on drivers ofland cover change, Jessie Stanton, Maria Uriarte and CharlesYackulic for guidance on analyses in R, Jim Rohlf for insights onspatial autocorrelation, Elizabeth Simola for helping get the projectstarted, and Eleonora D�avalos and Leonardo Zurita for discussionson economic and social drivers of coca cultivation and deforestation.

’REFERENCES

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Forests and Drugs: Coca-Driven Deforestation in Tropical Biodiversity Hotspots

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