Modelling drivers of atlantic forest dynamics using geographically weighted regression

Abstract

Despite its ecological importance and anthropogenic pressures, only a few studies have modeled deforestation and regeneration dynamics within Brazil’s Atlantic Forest biome. In this article, we propose an econometric approach to model these landscape dynamics. Based on public available data, the model was first processed using a STEPWISE procedure in the SPSS Statistics software, with ad hoc selection of the most relevant model. Next, we used Geoda software to account for spatial dependence and compared its results to a geographically weighted regression executed in ArcGIS software using a 25-municipality neighborhood distance. The amount of forest remnants, percentage of private protected land, expansion of pastures and planted forests can significantly explain the dynamics of deforestation and regeneration in the Atlantic Forest. The geographically weighted regression improved the model adjustment, and also illustrated locations where model performance was not satisfactory, and demonstrated where variables were more or less significant. The model can be used to inform conservation policies. It can also be used to create scenarios for simulations, allowing assessment of how possible market and policy changes, such as cattle rising and reforestation suffering market pressures, and changes in the national Forestry Code, would impact future deforestation and regeneration rates.