Enxofre elementar adicionado em resíduo de mineração e sua influência no crescimento de arbóreas nativas / Influence of elemental sulfur added to mining residue on the growth of native trees

 A exploração do mineral vermiculita tem gerado grande volume de resíduos no entorno das mineradoras, se tornado um passivo ambiental significativo, com potencial poluente. Uma importante alternativa para minimizar os impactos provenientes desta atividade é o aproveitamento do resíduo para o cultivo de arbóreas de ocorrência na Caatinga. Objetivou-se avaliar a influência de proporções de enxofre elementar (S°) adicionado ao resíduo de vermiculita sobre o crescimento inicial de duas espécies arbóreas. Foram realizados dois experimentos, sendo um com a espécie Tabebuia aurea e outro para o Caesalpinea ferrea. Em cada experimento, os tratamentos foram constituídos por cinco proporções de enxofre elementar (0, 50, 100, 150 e 200g/kg) adicionado ao resíduo de vermiculita e um tratamento adicional correspondente ao solo sem resíduo e adubado de forma convencional. Realizou-se medições do diâmetro do caule e altura de planta a cada quinze dias, durante 105 dias de cultivo. Após este período, foram avaliados o número de folhas, índice de área foliar, massa seca da parte aérea e raiz, índice de qualidade de Dickson e a eficiência dos tratamentos. O resíduo de vermiculita, tem potencial para ser utilizado no cultivo da Tabebuia aurea e Caesalpinea ferrea, com eficiência superior a 60% e 100% respectivamente. Entretanto, a produção de massa seca da Tabebuia aurea é afetada de forma negativa com a adição de S° em proporção superior a 50g/kg. Para o Caesalpinea ferrea, a eficiência do tratamento é maximizada com a adição de 87 g/kg de enxofre elementar ao resíduo.

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

Statement of Second Brazilian Congress of Mechanical Ventilarion : part I

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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