Protected areas (PAs) now shelter 54% of the remaining forests of the Brazilian Amazon and contain 56% of its forest carbon. However, the role of these PAs in reducing carbon fluxes to the atmosphere from deforestation and their associated costs are still uncertain. To fill this gap, we analyzed the effect of each of 595 Brazilian Amazon PAs on deforestation using a metric that accounts for differences in probability of deforestation in areas of pairwise comparison. We found that the three major categories of PA (indigenous land, strictly protected, and sustainable use) showed an inhibitory effect, on average, between 1997 and 2008. Of 206 PAs created after the year 1999, 115 showed increased effectiveness after their designation as protected. The recent expansion of PAs in the Brazilian Amazon was responsible for 37% of the region's total reduction in deforestation between 2004 and 2006 without provoking leakage. All PAs, if fully implemented, have the potential to avoid 8.0 ± 2.8 Pg of carbon emissions by 2050. Effectively implementing PAs in zones under high current or future anthropogenic threat offers high payoffs for reducing carbon emissions, and as a result should receive special attention in planning investments for regional conservation. Nevertheless, this strategy demands prompt and predictable resource streams. The Amazon PA network represents a cost of US$147 ± 53 billion (net present value) for Brazil in terms of forgone profits and investments needed for their consolidation. These costs could be partially compensated by an international climate accord that includes economic incentives for tropical countries that reduce their carbon emissions from deforestation and forest degradation.Amazon Region Protected Areas | effectiveness | reducing emissions from deforestation and forest degradation | simulation model | opportunity cost

Fires in tropical forests release globally significant amounts of carbon to the atmosphere and may increase in importance as a result of climate change. Despite the striking impacts of fire on tropical ecosystems, the paucity of robust spatial models of forest fire still hampers our ability to simulate tropical forest fire regimes today and in the future. Here we present a probabilistic model of human-induced fire occurrence for the Amazon that integrates the effects of a series of anthropogenic factors with climatic conditions described by vapor pressure deficit. The model was calibrated using NOAA-12 night satellite hot pixels for 2003 and validated for the years 2002, 2004, and 2005. Assessment of the fire risk map yielded fitness values > 85% for all months from 2002 to 2005. Simulated fires exhibited high overlap with NOAA-12 hot pixels regarding both spatial and temporal distributions, showing a spatial fit of 50% within a radius of 11 km and a maximum yearly frequency deviation of 15%. We applied this model to simulate fire regimes in the Amazon until 2050 using IPCC's A2 scenario climate data from the Hadley Centre model and a business-as-usual (BAU) scenario of deforestation and road expansion from SimAmazonia. Results show that the combination of these scenarios may double forest fire occurrence outside protected areas (PAs) in years of extreme drought, expanding the risk of fire even to the northwestern Amazon by midcentury. In particular, forest fires may increase substantially across southern and southwestern Amazon, especially along the highways slated for paving and in agricultural zones. Committed emissions from Amazon forest fires and deforestation under a scenario of global warming and uncurbed deforestation may amount to 21 +/- 4 Pg of carbon by 2050. BAU deforestation may increase fires occurrence outside PAs by 19% over the next four decades, while climate change alone may account for a 12% increase. In turn, the combination of climate change and deforestation would boost fire occurrence outside PAs by half during this period. Our modeling results, therefore, confirm the synergy between the two Ds of REDD (Reducing Emissions from Deforestation and Forest Degradation in Developing Countries).

Climate change scenarios vary considerably over the Amazon region, with an extreme scenario projecting a dangerous (from the human perspective) increase of 3.88C in temperature and 30% reduction in precipitation by 2050. The impacts of such climate change on Amazonian land-use dynamics, agricultural production, and deforestation rates are still to be determined. In this study, the authors make a first attempt to assess these impacts through a systemic approach, using a spatially explicit modeling framework to project crop yield and land-use/land-cover changes in the Brazilian Amazon by 2050. The results show that, without any adaptation, climate change may exert a critical impact on the yields of crops commonly cultivated in the Amazon (e.g., soybean yields are reduced by 44% in the worst-case scenario). Therefore, following baseline projections on crop and livestock production, a scenario of severe regional climate change would cause additional deforestation of 181 000 km 2 (120%) in the Amazon and 240 000 km 2 (1273%) in the Cerrado compared to a scenario of moderate climate change. Putting an end to deforestation in the Brazilian Amazon forest by 2020 (and of the Cerrado by 2025) would require either a reduction of 26%-40% in livestock production until 2050 or a doubling of average livestock density from 0.74 to 1.46 head per hectare. These results suggest that (i) climate change can affect land use in ways not previously explored, such as the reduction of yields entailing further deforestation, and (ii) there is a need for an integrated/multidisciplinary plan for adaptation to climate change in the Amazon.

No abstract

The aim of the present study was to analyze the spatial pattern of cases of maxillofacial injuries caused by interpersonal violence, based on the location of the victim’s residence, and to investigate the existence of conditions of socio-spatial vulnerability in these areas. This is a cross-sectional study, using the data of victims attended in three emergency hospitals in Belo Horizonte-Brazil between January 2008 and December 2010. Based on the process of spatial signature, the socio-spatial condition of the victims was identified according to data from census tracts. The spatial distribution trends of the addresses of victims were analyzed using Kernel maps and Ripley’s K function. Multicriteria analysis was used to analyze the territorial insertion of victims, using a combination of variables to obtain the degree of socio-spatial vulnerability. The residences of the victims were distributed in an aggregated manner in urban areas, with a confidence level of 99%. The highest densities were found in areas of unfavorable socioeconomic conditions and, to a lesser extent, areas with worse residential and neighborhood infrastructure. Spatial clusters of households formed in slums with a significant level of socio-spatial vulnerability. Explanations of the living conditions in segregated urban areas and analysis of the concentration of more vulnerable populations should be a priority in the development of public health and safety policies.