Publicações

An upper fourth molariform of a Scelidotheriine ground sloth is reported from Cameiro Cave, which is located in the karstic region of Serra da Mesa (Goias). In this limestone cave the tooth was found at the base of a depositional sequence, associated with a 2 m thick debris flows deposit, which is covered by the lowest carbonate crust dated at approximately 200,000 yr BP. Detailed morphological description, stable isotope data and stratigraphic context are provided. This represents the first megafauna record for this region of Central Brazil, where this record is found in direct association with a diverse microvertebrate fauna.

The Caatinga biome covers an area of 844,453 km(2) and has enormous endemic biodiversity, with unique characteristics that make it an exclusive Brazilian biome. It falls within the earth’s tropical zone and is one of the several important ecoregions of Brazil. This biome undergoes natural lengthy periods of drought that cause losses in crop and livestock productivity, having a severe impact on the population. Due to the vulnerability of this ecosystem to climate change, livestock has emerged as the main livelihood of the rural population, being the precursor of the replacement of native vegetation by grazing areas. This study aimed to measure GHG emissions from two different soil covers: native forest (Caatinga) and pasture in the municipality of Sao Joao, Pernambuco State, in the years 2013 and 2014. GHG measurements were taken by using static chamber techniques in both soil covers. According to a previous search, so far, this is the first study measuring GHG emissions using the static chamber in the Caatinga biome. N2O emissions ranged from -1.0 to 4.2 mg m(-2) d(-1) and -1.22 to 3.4 mg m(-2) d(-1) in the pasture and Caatinga, respectively, and they did not significantly differ from each other. Emissions were significantly higher during dry seasons. Carbon dioxide ranged from -1.1 to 14.1 and 1.2 to 15.8 g m(-2) d(-1) in the pasture and Caatinga, respectively. CO2 emissions were higher in the Caatinga in 2013, and they were significantly influenced by soil temperature, showing an inverse relation. Methane emission ranged from 6.6 to 6.8 and -6.0 to 4.8 mg m(-2) d(-1) in the pasture and Caatinga, respectively, and was significantly higher only in the Caatinga in the rainy season of 2014. Soil gas fluxes seemed to be influenced by climatic and edaphic conditions as well as by soil cover in the Caatinga biome.

Human-induced climate change is considered a conspicuous threat to biodiversity in the 21st century. Species’ response to climate change depends on their exposition, sensitivity and ability to adapt to novel climates. Exposure to climate change is however uneven within species’ range, so that some populations may be more at risk than others. Identifying the regions most exposed to climate change is therefore a first and pivotal step on determining species’ vulnerability across their geographic ranges. Here, we aimed at quantifying mammal local exposure to climate change across species’ ranges. We identified areas in the Brazilian Amazon where mammals will be critically exposed to non-analogue climates in the future with different variables predicted by 15 global circulation climate forecasts. We also built a null model to assess the effectiveness of the Amazon protected areas in buffering the effects of climate change on mammals, using an innovative and more realistic approach. We found that 85% of species are likely to be exposed to non-analogue climatic conditions in more than 80% of their ranges by 2070. That percentage is even higher for endemic mammals; almost all endemic species are predicted to be exposed in more than 80% of their range. Exposure patterns also varied with different climatic variables and seem to be geographically structured. Western and northern Amazon species are more likely to experience temperature anomalies while northeastern species will be more affected by rainfall abnormality. We also observed an increase in the number of critically-exposed species from 2050 to 2070. Overall, our results indicate that mammals might face high exposure to climate change and that protected areas will probably not be efficient enough to avert those impacts.

The availability of accurate rainfall data with high spatial resolution, especially in vast watersheds with low density of ground-measurements, is critical for planning and management of water resources and can increase the quality of the hydrological modeling predictions. In this study, we used two classical methods: the optimal interpolation and the successive correction method (SCM), for merging ground-measurements and satellite rainfall estimates. Cressman and Barnes schemes have been used in the SCM in order to define the error covariance matrices. The correction of bias in satellite rainfall data has been assessed by using four different algorithms: (1) the mean bias correction, (2) the regression equation, (3) the distribution transformation, and (4) the spatial transformation. The satellite rainfall data were provided by the Tropical Rainfall Measuring Mission, over the Brazilian Amazon Rainforest. Performances of the two merging data techniques are compared, qualitatively, by visual inspection and quantitatively, by a statistical analysis, collected from January 1999 to December 2010. The computation of the statistical indices shows that the SCM, with the Cressman scheme, provides slightly better results.

The present study assesses mercury biomagnification and the trophic structure of the ichthyofauna from the Puruzinho Lake, Brazilian Amazon. In addition to mercury determination, the investigation comprised the calculation of Trophic Magnification Factor (TMF) and Trophic Magnification Slope (TMS), through the measurements of stable isotopes of carbon (δ13C) and nitrogen (δ15N) in fish samples. These assessments were executed in two different scenarios, i.e., considering (1) all fish species or (2) only the resident fish (excluding the migratory species). Bottom litter, superficial sediment and seston were the sources used for generating the trophic position (TP) data used in the calculation of the TMF. Samples from 84 fish were analysed, comprising 13 species, which were categorized into four trophic guilds: iliophagous, planktivorous, omnivorous and piscivorous fish. The δ13C values pointed to the separation of the ichthyofauna into two groups. One group comprised iliophagous and planktivorous species, which are linked to the food chains of phytoplankton and detritus. The other group was composed by omnivorous and piscivorous fish, which are associated to the trophic webs of phytoplankton, bottom litter, detritus, periphyton, as well as to food chains of igapó (blackwater-flooded Amazonian forests). The TP values suggest that the ichthyofauna from the Puruzinho Lake is part of a short food web, with three well-characterized trophic levels. Mercury concentrations and δ13C values point to multiple sources for Hg input and transfer. The similarity in Hg levels and TP values between piscivorous and planktivorous fish suggests a comparable efficiency for the transfer of this metal through pelagic and littoral food chains. Regarding the two abovementioned scenarios, i.e., considering (1) the entire ichthyofauna and (2) only the resident species, the TMF values were 5.25 and 4.49, as well as the TMS values were 0.21 and 0.19, respectively. These findings confirm that Hg biomagnifies through the food web of Puruzinho Lake ichthyofauna. The migratory species did not significantly change mercury biomagnification rate in Puruzinho Lake; however, they may play a relevant role in Hg transport. The biomagnification rate (TMS value) in Puruzinho Lake was higher than the average values for its latitude, being comparable to TMS values of temperate and polar systems (marine and freshwater environments).

Effluents from intensive cattle breeding and treated with anaerobic reactors are highly relevant for agricultural reuse. Current paper assesses the growth of sugarcane cultivars SP 803280 and RB 867515 for 220 days. Doses 0, 16, 48, 64, 80 and 96 kg ha-1 N were applied derived from biofertilizing sources produced by cattle-breeding waste water and urea. Experimental design comprised randomized blocks with 96 splits and four replications. The cultivars fertilized with biofertilizers caused growth similar to those fertilized with urea. Doses 64, 80 and 96 kg ha-1 had the best efficiency in growth. Since average height of 3 m was obtained for cultures at the end of the experiment, urea may be replaced by biofertilizers.

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman-Monteith and Shuttleworth-Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80% of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65% of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy-atmosphere “coupling” was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land-surface-atmosphere exchange parameterizations across a range of spatial scales.

Species distribution models (SDMs) are tools to obtain habitat suitability maps based on historical species occurrences and environmental variables. Those maps can be used to restrict fishing grounds or to assist in planning and reserve selection. This is especially important for species at risk of extinction. We developed SDMs for five endangered elasmobranch species, namely Squatina guggenheim, S. occulta, Rhinobatos horkelii, Galeorhinus galeus, and Mustelus schmitti, using Boosted Regression Trees. Data from 1,704 bottom trawls carried out between 1972 and 2005 as part of research surveys on the southern Brazilian shelf between 28°36′S and 33°45′S, combined with satellite imagery and environmental atlases, were used in the models. Based on 10-fold cross-validation statistics, all models had a reasonable performance, though S. guggenheim models had an excellent discrimination (AUC > 0.9) and R. horkelii models had just a fair discriminatory power (AUC 0.70.8). Except for R. horkelii, all models showed good association between observed and predicted occurrences (PBC > 0.5). Squatina guggenheim models provided the greatest explained deviance (4954%), whereas R. horkelii models the smallest (1417%). Models predictions were consistent with the current knowledge of all species. Moreover, those models made reasonable predictions using the great spatial and temporal coverage of satellite data.

Various fractions of particulate matter have been associated with increased mortality and morbidity. The purpose of our study is to analyze the associations between concentrations of PM2.5, PM2.5-10, PM10 and their chemical constituents (soluble ions) with hospital admissions due to circulatory and respiratory diseases among the elderly in a medium-sized city in Brazil. A time series study was conducted using Poisson regression with generalized additive models adjusted for confounders. Statistically significant associations were identified between PM10 and PM2.5-10 and respiratory diseases. Risks of hospitalization increased by 23.5% (95% CI: 13.5; 34.3) and 12.8% (95% CI: 6.0; 20.0) per 10 μg/m3 of PM2.5-10 and PM10, respectively. PM2.5 exhibited a significant association with circulatory system diseases, with the risk of hospitalization increasing by 19.6% (95% CI: 6.4; 34.6) per 10 μg/m3. Regarding the chemical species; SO4 2-, NO3 -, NH4 + and K+ exhibited specific patterns of risk, relative to the investigated outcomes. Overall, SO4 2- in PM2.5-10 and K+ in PM2.5 were associated with increased risk of hospital admissions due to both types of diseases. The results agree with evidence indicating that the risks for different health outcomes vary in relation to the fractions and chemical composition of PM10. Thus, PM10 speciation studies may contribute to the establishment of more selective pollution control policies.

Para a elaboração do plano de bacia se faz necessária a realização de estimativas da resposta hidrológica. Sendo assim, o objetivo desse estudo foi de avaliar a simulação do comportamento hidrológico da bacia hidrográfica do Alto Canoas localizada em Santa Catarina, através de redes neurais artificiais Multi Layer Perceptron (MLP), bem como de analisar a contribuição das variáveis de entrada para a modelagem. Foram testados doze tratamentos com combinações de variáveis de precipitação, evapotranspiração (ET0) e vazão, além de transformações e deslocamentos temporais dessas, a fim de determinar as variáveis que promovessem o melhor desempenho da modelagem da vazão. A MLP foi treinada em modo open-loop utilizando parte das vazões observadas. As vazões foram simuladas em open-loop e closed-loop para o período de teste, sendo em closed-loop utilizado a vazão simulada no passo de tempo anterior como entrada. O algoritmo de aprendizado utilizado foi o de Levenberg-Marquardt. O tratamento que apresentou melhor desempenho (Nash e Sutcliffe (NS) = 0,9119, Raiz do Erro Médio Quadrático (RMS) = 14,29 m3 /s) empregou a precipitação diária das quatro estações pluviométricas (Urubici, Vila Canoas, Lomba Alta e Anitápolis), precipitação das quatro estações com tempo de resposta de -2 dias, e vazão simulada do dia anterior. Apesar do baixo RMS, a vazão modelada pela MLP foi, em geral, superestimada.

The scale, rate, and intensity of humans environmental impact has engendered broad discussion about how to find plausible pathways of development that hold the most promise for fostering a better future in the Anthropocene. However, the dominance of dystopian visions of irreversible environmental degradation and societal collapse, along with overly optimistic utopias and business-as-usual scenarios that lack insight and innovation, frustrate progress. Here, we present a novel approach to thinking about the future that builds on experiences drawn from a diversity of practices, worldviews, values, and regions that could accelerate the adoption of pathways to transformative change (change that goes beyond incremental improvements). Using an analysis of 100 initiatives, or seeds of a good Anthropocene, we find that emphasizing hopeful elements of existing practice offers the opportunity to: (1) understand the values and features that constitute a good Anthropocene, (2) determine the processes that lead to the emergence and growth of initiatives that fundamentally change humanenvironmental relationships, and (3) generate creative, bottom-up scenarios that feature well-articulated pathways toward a more positive future.