Predictors of deforestation in the Brazilian Amazon
Article first published online: 4 JUL 2002
Journal of Biogeography
Volume 29, Issue 5-6, pages 737–748, May/June 2002
How to Cite
Laurance, W. F. , Albernaz, A. K. M. , Schroth, G., Fearnside, P. M. , Bergen, S., Venticinque, E. M. and Da Costa, C. (2002), Predictors of deforestation in the Brazilian Amazon. Journal of Biogeography, 29: 737–748. doi: 10.1046/j.1365-2699.2002.00721.x
- Issue published online: 4 JUL 2002
- Article first published online: 4 JUL 2002
- government policy;
- habitat fragmentation;
- human population size;
- soil fertility
Aim and Location
We assessed the effects of biophysical and anthropogenic predictors on deforestation in Brazilian Amazonia. This region has the world's highest absolute rates of forest destruction and fragmentation.
Using a GIS, spatial data coverages were developed for deforestation and for three types of potential predictors: (1) human-demographic factors (rural-population density, urban-population size); (2) factors that affect physical accessibility to forests (linear distances to the nearest paved highway, unpaved road and navigable river), and (3) factors that may affect land-use suitability for human occupation and agriculture (annual rainfall, dry-season severity, soil fertility, soil waterlogging, soil depth). To reduce the effects of spatial autocorrelation among variables, the basin was subdivided into >1900 quadrats of 50 × 50 km, and a random subset of 120 quadrats was selected that was stratified on deforestation intensity. A robust ordination analysis (non-metric multidimensional scaling) was then used to identify key orthogonal gradients among the ten original predictor variables.
The ordination revealed two major environmental gradients in the study area. Axis 1 discriminated among areas with relatively dense human populations and highways, and areas with sparse populations and no highways; whereas axis 2 described a gradient between wet sites having low dry-season severity, many navigable rivers and few roads, and those with opposite values. A multiple regression analysis revealed that both factors were highly significant predictors, collectively explaining nearly 60% of the total variation in deforestation intensity (F2,117=85.46, P < 0.0001). Simple correlations of the original variables were highly concordant with the multiple regression model and suggested that highway density and rural-population size were the most important correlates of deforestation.
These trends suggest that deforestation in the Brazilian Amazon is being largely determined by three proximate factors: human population density, highways and dry-season severity, all of which increase deforestation. At least at the spatial scale of this analysis, soil fertility and waterlogging had little influence on deforestation activity, and soil depth was only marginally significant. Our findings suggest that current policy initiatives designed to increase immigration and dramatically expand highway and infrastructure networks in the Brazilian Amazon are likely to have important impacts on deforestation activity. Deforestation will be greatest in relatively seasonal, south-easterly areas of the basin, which are most accessible to major population centres and where large-scale cattle ranching and slash-and-burn farming are most easily implemented.