DOI: 10.5061/dryad.1hb1f
title: Data from: Deforestation and coca cultivation rooted in twentieth-century development projects

description: 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.
description: Colonization
 projectsSpreadsheet 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.csvGrid dataSpreadsheet 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 1990/1992grid.csvZip file of municipalities in shapefile formatShapefile of municipality units used in modeling.mpios.zipDeforestation rates by studySpreadsheet 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.csvR script for INLA modelingTakes 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.Rdatad2pro3_hires.RR script to make predictions based on INLA modelingObtains 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.RdataHiRes.RR script to plot logistic modelLoads 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.Rdataplot_decay.RR script to plot deforestation ratesTakes 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.csvdefrates.R