Most of the world's coca—the source of cocaine—is grown in the Amazonian forests of Colombia, Peru, and Bolivia. As cultivation continues despite eradication, a shift to giving farmers more incentives to abandon coca is currently proposed. Assuming coca cultivation is an important cause of migration and deforestation, new alternative development projects also aim to conserve forests. We show coca cultivation strongly increases near never-completed 1960s–1970s state-sponsored projects to settle the Amazon. Improved roads and colonization projects opened the western Amazon frontier to migration, generating deforestation and, once support dwindled, setting the stage for coca cultivation. New studies also show coca cultivation generates negligible direct or indirect forest loss and fails to explain migration, whereas expanding legal agriculture, roads, displacement, and eradication increase deforestation. These findings highlight the urgent need to both commit development investment for the long term and set explicit conservation goals in western Amazonia.
Colonization projects
Spreadsheet of project data used for modeling. id - code, ColPro -development and colonization project name,
Country -country of project, Colpro2 -another development and colonization project name.
cpn.csv
Grid data
Spreadsheet of pixel data used for modeling. POINTID -pixel id,GRID_CODE -codr on grid, X_DD -longitude decimal degree, Y_DD -latitude decimal degree, NEAR_FID -id of nearest project, NEAR_DIST -nearest project distance, NEAR_X -nearest horizontal distance,NEAR_Y -nearest vertical distance, coca -absence (0) or presence (1) of coca in 2014, coca1992 -absence (0) or presence (1) of coca in 1990/1992
grid.csv
Zip file of municipalities in shapefile format
Shapefile of municipality units used in modeling.
mpios.zip
Deforestation rates by study
Spreadsheet of deforestation rates caused by coca or other crops. forest - kind of forest, type -old-growth (primary) or
secondary forest, loss - quantity lost during time step, start - starting forest quantity, defrate_old - Puyravaud 2004 deforestation rate, defrate -revised deforestation rate in which positive values represent loss, illicit -rate is from coca (1) or legal use (0), study -source study. Based on: Armenteras, D., Rodriguez, N. & Retana, J. 2013 Landscape dynamics in
northwestern Amazonia: an assessment of pastures, fire and illicit crops as drivers of tropical deforestation. PLoS ONE 8, e54310.
Chadid, M., Davalos, L., Molina, J. & Armenteras, D. 2015 A Bayesian Spatial Model Highlights Distinct Dynamics in Deforestation from Coca and Pastures in an Andean Biodiversity Hotspot. Forests 6, 3828.
Davalos, L. M., Bejarano, A. C., Hall, M. A., Correa, H. L., Corthals, A. & Espejo, O. J. 2011 Forests and Drugs: Coca-Driven Deforestation in Tropical Biodiversity Hotspots. Environ. Sci. Technol. 45, 1219-1227.
(10.1021/es102373d.)
UNODC & Peru Ministerio del Medio Ambiente. Analisis economico de las actividades causantes de la deforestacion en Pichis-Palcazu. Lima: UNODC and Ministerio del Medio Ambiente, Peru2011.
ic_noic.csv
R script for INLA modeling
Takes in data in cpn.csv, grid.csv, and mpios.shp and produces map.graph for defining neighboring spatial units, runs and prints spatially explicit binomial models of Table 2. Saves results as d_models3.Rdata
d2pro3_hires.R
R script to make predictions based on INLA modeling
Obtains predictions from models of Table 2, runs AUC analyses of pixel predictions, and prints predictions of model without predictor (no influence of distance to projects), model using the nearest project as random effect along with influence of distance to projects (pred2), and the model
using municipality intercepts as random effects along with with influence of distance to projects (pred6). The latter is the best-fit model. The script also saves the three-model results as GeoTIFF rasters, the best model is listed as best_auc_hr.tif. NOTE: This R script must run after d2pro3_hires.R, or after loading d_models3.Rdata
HiRes.R
R script to plot logistic model
Loads the data generated by script d2pro3_hires.R and generates a decay function plot based on the posteriors of the relationship between probability of finding coca by pixel and the log10 distance to the nearest development project. It also plots the observations. NOTE: this script must run after d2pro3_hires.R, or after loading d_models3.Rdata
plot_decay.R
R script to plot deforestation rates
Takes in data from ic_noic.csv, and makes a bar plot of
deforestation rates from different studies and locations. NOTE: this R script must run in the same folder as the data ic_noic.csv
defrates.R