Spatial determinants of Atlantic Forest loss and recovery in Brazil
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Despite continued forest cover losses in many parts of the world, Atlantic Forest, one of the largest of the Americas, is increasing in some locations. Economic factors are suggested as causes of forest gain, while enforcement has reduced deforestation.
We examine three aspects of this issue: the relative importance of biophysical versus anthropogenic factors in driving forest dynamics; role of forest mean patch age influencing areas targeted for losses; and what future forest mean patch age mosaic we can expect (more forest cover and full forest maturity?).
Three land cover maps from 1990, 2000 and 2010, were used in the study. We selected six biophysical and six anthropogenic spatial determinants to analyze by means of weights of evidence, using Dinamica software.
Results show that forest regrowth is influenced by multiple factors, working in synergy. Biophysical variables are related to forest gain while anthropogenic are associated with loss. Clear patterns of regrowth on pasture and sugarcane plantations occurred, especially near rivers and forest patches, on steeper slopes and with sufficient rainfall. Forest loss has targeted both older and newer forests. Future projections reveal forest gain in a slow pace, followed by specific ecosystem service losses, due to continuous trends of older mature forest loss.
Regrowth is linked to land abandonment, and to neighboring environmental conditions. It is important to question which mechanisms will guarantee and potentiate new regrowth, thus contributing to landscape restoration and reestablishment of ecosystem services in the Atlantic Forest.
KeywordsForest transition Drivers Regrowth Sugarcane Pastureland Weights of evidence
This study was supported by FAPESP (São Paulo Research Foundation), under project number 2010/19670-8. The authors would also like to thank the CSR (Remote Sensing Center at Federal University of Minas Gerais), for their support on the Dinamica EGO software. We also thank graduate student Renata Siqueira Melo, for her support on running the sample plots.
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