Abstract
Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.
Highlights
The Amazon is the largest remaining continuous tropical rainforest on Earth
Change in temporary agriculture area was non-significant in both cases, whereas change in agricultural gross domestic product (GDP) was found to only be significant in the period post-PPCDAM
The cumulative prediction accuracy improved through time, for the time span that validation data is available, with an average of 15% perfect match between predicted and observed deforestation by 2010 (Fig. 3a).These results suggest that the model is correctly predicting the general spatial pattern of deforestation, but that the ability to predict the exact sequence of deforestation events is very poor
Summary
The Amazon is the largest remaining continuous tropical rainforest on Earth. It covers about 6 million square kilometres and crosses nine nations’ boundaries. Brazil is the country that hosts the largest portion (about 60% of the area) of the Amazon This region is characterized by its high cultural and biological diversity[1], but by 2009 already 19% of its forest cover had been converted to other land uses[2]. The rate of deforestation – that is, the area deforested per year – in the Brazilian Amazon is highly variable [6]. These fluctuations are related to several factors such as the economic health of the country, infrastructure development, and the world’s demand for agricultural products, such as beef or soybeans[6,7,8,9]. Governance through command and control, restriction to rural credits and expansion of protected areas, helped by a global economic crisis, seem to have contributed to reduce deforestation[10] going in an opposite trend to Brazil’s economic growth[11]. these regional and global factors influence the deforestation rates in the Brazilian Amazon, deforestation is the sum of thousands of local deforestation events, which occur with an intensity that varies greatly across the region due to many factors including physiographic attributes, access to infrastructure, human population characteristics and dynamics, and socioeconomic organization[12]
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