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

Fatores associados à adequação do cuidado pré-natal e à assistência ao parto em São Tomé e Príncipe, 2008-2009

Neste artigo, foram identificados fatores sociodemográficos associados com o cuidado pré-natal e com a assistência ao parto em São Tomé e Príncipe. A amostra foi composta por 1.326 nascidos vivos de mulheres de 15 a 49 anos que participaram do Inquérito Demográfico e Sanitário de São Tomé e Príncipe, 2008-2009. Foram utilizados modelos de regressão logística e multinomial multiníveis. A adequação global do cuidado pré-natal foi de 26% e da assistência ao parto de 7% quando realizado por médicos e de 76% quando realizado por enfermeiras/auxiliares. Os fatores associados ao pré-natal e à assistência ao parto adequados foram: ordem de nascimento, educação materna e o índice de bem-estar econômico. O local de residência se mostrou fator importante apenas em relação à assistência ao parto. Observou-se que os efeitos aleatórios referentes às áreas onde as mulheres residem exerceram impacto importante sobre a chance de realizar pré-natal adequado e parto com profissionais capacitados. A importância dos fatores socioeconômicos aponta para a elaboração de ações que visem reduzir a desigualdade social em São Tomé e Príncipe

Fatores associados à adequação do cuidado pré-natal e à assistência ao parto em São Tomé e Príncipe, 2008-2009

Neste artigo, foram identificados fatores sociodemográficos associados com o cuidado pré-natal e com a assistência ao parto em São Tomé e Príncipe. A amostra foi composta por 1.326 nascidos vivos de mulheres de 15 a 49 anos que participaram do Inquérito Demográfico e Sanitário de São Tomé e Príncipe, 2008-2009. Foram utilizados modelos de regressão logística e multinomial multiníveis. A adequação global do cuidado pré-natal foi de 26% e da assistência ao parto de 7% quando realizado por médicos e de 76% quando realizado por enfermeiras/auxiliares. Os fatores associados ao pré-natal e à assistência ao parto adequados foram: ordem de nascimento, educação materna e o índice de bem-estar econômico. O local de residência se mostrou fator importante apenas em relação à assistência ao parto. Observou-se que os efeitos aleatórios referentes às áreas onde as mulheres residem exerceram impacto importante sobre a chance de realizar pré-natal adequado e parto com profissionais capacitados. A importância dos fatores socioeconômicos aponta para a elaboração de ações que visem reduzir a desigualdade social em São Tomé e Príncipe