The use of species distribution models to predict the spatial distribution of deforestation in the western Brazilian Amazon

摘要

The prevention of deforestation in rainforests requires the identification of where facilitating and mitigating factors will combine and increase the likelihood of deforestation. This approach, which relates a geographic space with an environmental space of factors to predict where new deforestation will occur, is very similar to the approaches used to predict species distributions. Thus, we believe that deforestation can be treated as a "species" and that its future occurrence can be determined using species distribution models. The objective of this work is to test the efficiency of species distribution models in predicting the potential areas of deforestation in a region of the western Amazon. We analyzed five different areas in the arc of deforestation. For each area, we ran the MaxEnt model in six different experiments to determine the boundaries of the probability distributions. Potential areas identified using the different models of MaxEnt were very effective in predicting deforestation areas. The models that used only previous deforestation density were less effective than the models that included functional variables. In four of the five areas, 80% of the new deforestation occurred in the area predicted by the models. These models were more effective than the business-as-usual (BAU) and governance (GOV) model scenarios described using the DINAMICA-EGO platform by Soares-Filho et al. (2006). Species distribution models are a valuable tool for determining potential areas of future Amazon deforestation. The use of these models arises as support to efforts to conserve tropical forests and identify critical locations where command and control actions against deforestation can be most efficient.