Predictors of deforestation in the Brazilian Amazon

Abstract

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.Methods 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.Results 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 (F 2,117 ¼ 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.Main conclusions These trends suggest that deforestation in the Brazilian Amazon is being largely determined by three proximate factors: human population density, highways and dryseason 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-andburn farming are most easily implemented.

Deforestation modelling using logistic regression and GIS

“…One of the most important methods to detect the factors influencing deforestation and their spatial interaction is to model their influence on the landscape using spatial data (Serneels, Lambin 2001;Laurance et al 2002;Nagendra et al 2003;Mertens et al 2004;Etter et al 2006).…”

Causes of habitat loss in a Neotropical landscape: The Panama Canal corridor

“…In our study area, low mean annual precipitation means drier seasonal forests compared to thicker "jungles" in more humid subregions (Condit, 1998;Rompre et al, 2007). Likewise, in Amazonia, deforestation is greater in seasonal areas and regions with low precipitation (Fernside and Ferraz, 1995;Steininger et al, 2001;Laurance et al, 2002a). Our results, excluding Colon, which is the only large city on the Caribbean coast of the entire country (Suarez, 1981;MIVI, 2005), indicate urbanization clearly occurs in the drier subregions of the study area.…”

“…Our results, excluding Colon, which is the only large city on the Caribbean coast of the entire country (Suarez, 1981;MIVI, 2005), indicate urbanization clearly occurs in the drier subregions of the study area. Complex topography is often associated with more mature-intact forests than areas with flatter terrain, because ease of access for harvesting and development is limited (Kerr and Packer, 1997;Steininger etal., 2001;Veldkamp and Lambin, 2001;Laurance et al, 2002a;Rompre et al, 2007). In this study, areas with flatter terrain tend to occur on the drier slope where forests are usually younger and more disturbed by human activities (Condit, 1998;Pyke et al, 2001).…”

“…Data were interpolated for each subregion and verified by experts from the government. As Laurance et al (2002a) did, we distinguished urban and rural populations, because urban population size and density are far greater than their rural counterparts, and both affect habitat loss in different ways (Laurance et al, 2001(Laurance et al, , 2002a(Laurance et al, , 2006. We used population size, not density, for urbanized areas because the latter was much less variable than the former.…”

“…Studies of birds, for example, show that both fragmentation and habitat loss reduce diversity (Robinson et al, 2000(Robinson et al, , 2004Laurance et al, 2002b;Gaston et al, 2003;Waltert et al, 2005;Rompre et al, 2007). Major factors associated with fragmentation and habitat loss, and therefore loss of biodiversity, are high levels of human population density (Cincotta et al, 2000;McKee et al, 2003;Scharlemann et al, 2004), density of roads (Maki et al, 2001;Laurance et al, 2002aLaurance et al, , 2006Gutzwiller and Barrow, 2003;Glennon and Porter, 2005), changes in agricultural practices (Meyer and Turner, 1992;Huston, 1993), logging (Kohleretal., 2002;Rudel, 2005), and hunting (Brashares et al, 2001;Laurance et al, 2006).…”