GIS can be a useful tool for spatial or land-use planning, but only if several conditions are fulfilled. The key conditions are related to 1) the quality of basic spatial information, and 2) the statistical methods applied to the spatial nature of the data. Appropriate information and methods allow the generation of robust models that guarantee objective and methodologically sound decisions. In this study we apply several multivariate statistical methods and test their usefulness to provide robust solutions in forestry planning using GIS. We must emphasize that in our Iberian study area, where forests have progressively decreased in extent over centuries, the main aims of forestry planning are the reduction of forest fragmentation, biodiversity conservation, and restoration of degraded biotopes. The research develops a set of likelihood or suitability models for the presence of tree species that are widely distributed over a study area of 41,000 km2. The utility of suitability models has been demonstrated in some previous studies1, but they are still not as widely employed as might be expected. A suitability model is a raster map in which each pixel is assigned a value reflecting suitability for a given use (e.g., presence of a tree species). Suitability models can be generated through diverse techniques, such as logistic regression or non-parametric CART (classification and regression trees) and MARS (multiple adaptive regression splines)2-4. All of these techniques require a vegetation map (dependent variable) and a set of environmental variables (climate, topography, geology, etc.) which potentially influence the vegetation distribution. The foundation of the method is to establish relationships between the environmental variables and the spatial distribution of the vegetation. Typically, each vegetation type will respond in a different way as a consequence of its contrasting environmental requirements.