Only 7 % of the Atlantic Forest Biodiversity Hotspot is currently protected, though it holds 18 % of all amphibian species in South America. How effective would the Atlantic Forest network of protected areas (PAs) be in a changing climate? Are there some intrinsic features of PAs that drive species loss or gain inside them? We addressed these questions by modeling the ecological niches of 430 amphibian species in the Atlantic Forest and projecting their distributions into three future climate change simulations. We then assessed changes in species richness inside PAs for different time frames and tested their significance via null model. The number of species should decline within Atlantic Forest network of PAs under changing climate conditions. Only altitude was a good predictor of species gains or lost inside PAs. Therefore, we suggest that new PAs established in highlands would be more effective to alleviate the effects of climate change on this imperiled fauna.

During the 2009 Conference of the Parties meeting in Copenhagen, the Brazilian government announced voluntary targets to reduce GHG emissions, with the targets being reconfirmed in Cancun (2010) and in Durban (2011). An estimate is presented of the economic impact of alternative policies to achieve such targets, including actions to cut emissions from deforestation and agricultural production. A dynamic-recursive general equilibrium model of the world economy is used. The main results show that deforestation emissions in Brazil can be reduced at very low costs, but the cost of cutting emissions from agriculture and energy use may lead to a 2.3% drop in gross domestic product by 2020 if sector-specific carbon taxes are applied. Such costs may be reduced to 1.5% under a carbon trading scheme. The negative impact of carbon taxes on agricultural production indirectly reduces deforestation rates; therefore, directly cutting emissions from deforestation is the most cost-effective option since it does not adversely affect agricultural production, which continues to expand into low-yield, underutilized pastures and secondary forest areas.

We developed a stochastic model for quantitative risk assessment for the Schistosoma mansoni(SM) parasite, which causes an endemic disease of public concern. The model provides answers in a useful format for public health decisions, uses data and expert opinion, and can be applied to any landscape where the snail Biomphalaria glabrata is the main intermediate host (South and Central America, the Caribbean, and Africa). It incorporates several realistic and case-specific features: stage-structured parasite populations, periodic praziquantel (PZQ) drug treatment for humans, density dependence, extreme events (prolonged rainfall), site-specific sanitation quality, environmental stochasticity, monthly rainfall variation, uncertainty in parameters, and spatial dynamics. We parameterize the model through a real-world application in the district of Porto de Galinhas (PG), one of the main touristic destinations in Brazil, where previous studies identified four parasite populations within the metapopulation. The results provide a good approximation of the dynamics of the system and are in agreement with our field observations, i.e., the lack of basic infrastructure (sanitation level and health programs) makes PG a suitable habitat for the persistence and growth of a parasite metapopulation. We quantify the risk of SM metapopulation explosion and quasi-extinction and the time to metapopulation explosion and quasi-extinction. We evaluate the sensitivity of the results under varying scenarios of future periodic PZQ treatment (based on the Brazilian Ministry of Health’s plan) and sanitation quality. We conclude that the plan might be useful to slow SM metapopulation growth but not to control it. Additional investments in better sanitation are necessary.

The rapid global decline of amphibian population is alarming because many occur for apparently unknown or enigmatic reasons, even inside protected areas (PAs). Some studies have predicted the effects of climate change on amphibians’ distribution and extinction, but the relationship and consequences of climate change to the phylogenetic structure of amphibian assemblages remain obscure. By applying robust techniques for ecological niche modeling and a cutting-edge approach on community phylogenetics, here, we evaluate how climate change affects the geographical pattern of amphibian species richness and phylogenetic diversity in the Atlantic Forest Biodiversity Hotspot, Brazil, as well as how the phylogenetic composition of amphibian assemblages respond to climate change. We found that most species contracted their ranges and that such responses are clade specific. Basal amphibian clades (e.g. Gymnophiona and Pipidae) were positively affected by climate change, whereas late-divergent clades (e.g. Cycloramphidae, Centrolenidae, Eleutherodactylidae, Microhylidae) were severely impacted. Identifying major changes in the phylogenetic pool represents a first step towards a better understanding of how assembly processes related to climate change will affect ecological communities. A deep analysis of the impacts of climate change not only on species, but also on the evolutionary relationships among species might foster the discussion on clade-level conservation priorities for this imperiled fauna.

Numerical studies on impacts of landscape fragmentation due to land use and land cover change (LUCC) on precipitation fields over the Ji-Paraná basin in the Amazon region are carried out using atmospheric Eta model. Experiments consider historical data about LUCC over the basin from 1978 to 2000 and compare simulations under LUCC conditions with simulations under pristine conditions. In agreement with previous observational studies, model results do not show statistically significant impacts on precipitation in the region. Results indicate that variability in precipitation in this region is mainly controlled by large-scale atmospheric characteristics and soil moisture conditions. However, some limitations are identified in the model simulations, mainly associated to the diurnal cycle of precipitation.

OBJECTIVE
To investigate the effects of fine particulate matter emitted through biomass burning on hospitalizations for respiratory diseases in children living in Manaus, Northern Brazil.

METHODS
Descriptive study with ecologic time series design carried out in Manaus from 2002 to 2009. Hospital admission data were obtained from the Unified Health System database. PM2.5 levels were estimated using aerosol remote sensing through the measurement of aerosol optical depth at a wavelength of 550 nm. Statistical methods were used in the data analysis, with Pearson correlation and multiple linear regression between variables, with a 95% confidence interval.

RESULTS
The region of Manaus showed low PM2.5 concentrations when compared to the Southern Amazonian region. Between August and November (dry period in the region), was when the highest mean levels of PM2.5, estimated between 18 to 23 µg/m3, and the largest number of fires were observed. For the rainy season, an average of 12 µg/m3, 66% lower than the dry season measurements (20.6 µg/m3) was observed. The highest rates of hospitalization were observed during the rainy season and April was the month with the highest levels at 2.51/1,000 children. A positive association between hospital admissions and relative humidity (R = 0.126; p-value = 0.005) was observed, while the association between admissions and PM2.5 was negative and statistically significant (R = -0.168; p-value = 0.003). The R 2 of the final model (Hospitalizations = 2.19*Humidity – 1.60*PM2.5 – 0.23*Precipitation) explained 84% of hospitalizations due to respiratory disease in children living in Manaus, considering the independent variables statistically significant (humidity, PM2.5, and precipitation).

CONCLUSIONS
Hospital admissions for respiratory diseases in children in Manaus, were more related to weather conditions and in particular relative humidity, than to exposure to aerosols emitted by biomass burning in the Amazonian region.