Morphometric characterization of the Poxim-Açu River sub-basin, Sergipe, Brazil

A caracterização morfométrica de uma bacia hidrográfica é um dos principais procedimentos executados em análises hidrológicas e ambientais para o entendimento de suas dinâmicas local e regional e apoiar o gerenciamento dos recursos hídricos. Este trabalho analisou as variáveis morfométricas da sub-bacia hidrográfica do rio Poxim-Açu, afluente do rio Poxim que contribui por aproximadamente 30% do abastecimento da capital sergipana. Para o Modelo Digital de Elevação (MDE) foram utilizados dados do SRTM (Shuttle Radar Topography Mission), compatíveis com a escala 1:250.000, disponibilizados pela Embrapa Monitoramento por Satélite, carta de articulação do IBGE SC-24-Z-B. Demais dados foram obtidos a partir do Atlas Digital dos Recursos Hídricos de Sergipe, processados em Sistemas de Informação Geográfica (SIG), o QGIS 1.6 e o ArcGIS 10.1 com extensões Spatial Analist e ArcHidro. Os resultados obtidos permitirão estudos ambientais mais acurados sobre esta bacia hidrográfica. Verificou-se que a sub-bacia possui uma área de 128 km2, índice de sinuosidade de 10%, hierarquia fluvial de 4ª ordem e coeficiente de compacidade de 1,76. Com forma alongada, é praticamente reta e com baixa tendência a picos de inundações. _________________________________________________________________________________________ ABSTRACT: Watershed morphometric characterization is one of the most used procedures in hydrological and environmental analysis, with the purpose of understanding local and regional dynamics to support water resource management. This paper analyzed the morphometric variables of the Poxim-Açu River sub-basin, affluent of the Poxim River, which contributes with approximately 30 % of the water supply of the capital of Sergipe. We used SRTM (Shuttle Radar Topography Mission) data for the Digital Elevation Model (DEM), with the scale of 1:250,000, provided by Embrapa Satellite Monitoring and the IBGE topographic chart SC-24-Z-B. Other data were obtained from the Digital Atlas of Water Resources of Sergipe, which were processed in Geographic Information Systems (GIS), as the QGIS 1.6 and ArcGIS 10.1 with Spatial Analyst and ArcHidro extensions. Results will support environmental studies of this watershed. We found that the sub-basin has an area of 128 km2 , sinuosity index of 10%, fluvial hierarchy of the 4 th order, and a compactness coefficient of 1.76. It is practically straight with an elongated shape and a low tendency to have peaks of floods

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Concordância entre informações do Cartão da Gestante e da memória materna sobre assistência pré-natal Agreement between information from the Pregnant Card and the mother's memory of antenatal care

Este estudo seccional objetivou verificar a concordância entre as informações prestadas por puérperas e as registradas nos cartões das gestantes sobre assistência pré-natal no Sistema Único de Saúde da Região Metropolitana da Grande Vitória, Espírito Santo, Brasil. Considerou-se uma população de estudo de 1.035 puérperas, entrevistadas em oito maternidades, onde os cartões foram copiados. A representatividade da amostra foi garantida pela estratificação segundo a proporção de nascidos vivos. Informações foram coletadas, processadas e submetidas aos testes de kappa e McNemar. Os níveis de concordância sobre assistência pré-natal foram predominantemente ruins (kappa < 0,20). Puérperas tendem a superestimar a quantidade de consultas pré-natais (McNemar = 51,73; valor de p = 0,001), afirmar doenças gestacionais, como diabetes, anemia, hipertensão e infecções urinárias, relatar a execução de exames laboratoriais e clínicos. Os resultados sugerem uma reflexão sobre dados utilizados para o planejamento de políticas em saúde pública materno-infantil, visto que há variação conforme a fonte de informação.<br>This cross-sectional study aimed to verify agreement between information given by mothers after delivery and data recorded on Pregnant Cards about antenatal care under the Brazilian Unified National Health System in the Metropolitan Region of Vitória, Espírito Santo State, Brazil. The study considered a population of 1,035 postpartum mothers interviewed in eight hospitals, where the cards were copied. The representativeness of the sample was guaranteedby stratification according to the proportion of births. Kappa and McNemar tests were carried out with the collected and processed information. Agreement levels regarding antenatal care were predominantly poor (kappa < 0.20). Mothers tend to: overestimate the number of antenatal visits (McNemar = 51.73; p-value = 0.001); affirm diseases during pregnancy, such as diabetes, anemia, hypertension and urinary infections; report the performance of laboratory tests; report the carrying out of clinical examinations. Results suggest the need to reflect on the type of data used for planning and implementing maternal and child public health polices, since data varies depending on the information source