Environmental predictors of forest change: An analysis of natural predisposition to deforestation in the tropical Andes region, Peru

Abstract The spatial patterns of deforestation are usually non-randomly distributed across the landscape. While anthropogenically driven processes are often addressed in land-use regulation policies and deforestation research, less attention is given to the environmental factors that influence tropical deforestation. This study investigates to what extent climate conditions (temperature and precipitation) and biophysical landscape characteristics (elevation, slope, soil type, forest type, and distance to rivers) facilitate or mitigate deforestation processes in Peru's tropical Andes. A Random Forest regression model was constructed for the entire Peruvian tropical Andes, and separate models were developed for some of the known direct deforestation drivers in the region (coca production, gold mining, and land-use by indigenous and non-indigenous communities). Soil type and precipitation were identified as the most important deforestation predictors when the entire Peruvian tropical Andes was considered, whereas distance to rivers was associated with deforestation by mining activities, and elevation and temperature with coca cultivation areas. Using the regression results, a Random Forest classification model was constructed to locate areas where the composition of environmental factors could either facilitate or mitigate deforestation processes. It was found that almost 85% of the forests classified as having high to very high probability to deforestation were located outside current protected areas. In order to increase conservation impacts, the results suggest that greater consideration should be given to the distribution of environmental factors when designing land-use regulation policies and establishing protected areas.