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

Expanding tropical forest monitoring into Dry Forests: The DRYFLOR protocol for permanent plots

This is the final version. Available on open access from Wiley via the DOI in this recordSocietal Impact Statement Understanding of tropical forests has been revolutionized by monitoring in permanent plots. Data from global plot networks have transformed our knowledge of forests’ diversity, function, contribution to global biogeochemical cycles, and sensitivity to climate change. Monitoring has thus far been concentrated in rain forests. Despite increasing appreciation of their threatened status, biodiversity, and importance to the global carbon cycle, monitoring in tropical dry forests is still in its infancy. We provide a protocol for permanent monitoring plots in tropical dry forests. Expanding monitoring into dry biomes is critical for overcoming the linked challenges of climate change, land use change, and the biodiversity crisis.Newton FundNatural Environment Research Council (NERC)Fundação de Amparo à Pesquisa do Estado de São PauloCYTE

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

Avaliação do sensor de contratilidade cardíaca em sistema DDDR: estudo multicêntrico

Introdução: O tratamento de distúrbios na condução atrioventricular associados a doenças do nó sinusal com o emprego de marcapassos DDDR tem incentivado a procura de um sensor ideal. Objetivo: Avaliar a resposta de freqüência do marcapasso com sensor de contratilidade em situações de esforço físico e mental, tanto em laboratório como em atividades diárias de pacientes com bradicardia e insuficiência cronotrópica. Casuística e Métodos: Do estudo multicêntrico brasileiro "Projeto Inos DR _ Brasil", que emprega um sistema de estimulação DDDR cujo indicador é o estado contrátil do miocárdio, foram selecionados 38 pacientes com insuficiência cronotrópica, sendo 21 do sexo masculino e 17 do sexo feminino, com idades variando de 13 a 83 anos (média de 57 anos). O marcapasso utiliza um parâmetro do próprio controle cardiovascular (contratilidade cardíaca obtida pela medida da impedância cardíaca unipolar) para a adaptação da freqüência cardíaca, num sistema de malha fechada que, teoricamente, possibilita um ajuste a todas as necessidades fisiológicas. A calibração e programação do sistema só foi realizada 30 dias após o implante (tempo de maturação da irterface coração-eletrodo), realizando-se, então, teste de estresse mental (matemático) e teste ergométrico (em esteira), monitorados com histograma de freqüência e com curvas de consumo de oxigênio. Resultados: A média de limiares agudos de estimulação foi de 0,82 Volts e 0,55 Volts, e a média de limiares de sensibilidade foi de 2,37 mV e 10,61 mV, respectivamente, para átrios e ventrículos. A média de limiares crônicos de estimulação foi de 1,44 Volts e 1,18 Volts, e a média de limiares de sensibilidade foi de 2,81 mV e 6,3 mV, respectivamente para átrios e ventrículos. A freqüência cardíaca variou de 5% a 128% nas atividades físicas e de 5% a 80% nas atividades mentais, com elevação logo no início das atividades, permitindo uma curva normal de consumo de oxigênio, comparável à de indivíduos normais de mesma faixa etária, sexo e peso. As médias foram comparadas pelo teste T de Student e as variáveis, pela análise de variância. Conclusão: O sensor de contratilidade cardíaca tem excelente desempenho na adaptação da freqüência cardíaca, com valor semelhante ao produzido pelo sistema nervoso autônomo de indivíduos normais