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

Avaliação da qualidade das prescrições de enfermagem em hospitais de ensino público Evaluación de la calidad de las prescripciones enfermeras en hospitales de enseñanza pública Assessment of quality of nursing prescriptions in public teaching hospital

Estudo multicêntrico, transversal, realizado entre dezembro de 2009 e junho de 2010, que teve como objetivo avaliar a qualidade das Prescrições de Enfermagem (PE) em dois hospitais de ensino público. A amostra se constituiu de 1.307 PE, e os dados foram tratados por meio dos testes G e Qui-quadrado. A determinação da qualidade das PE se baseou nos Índices de Classificação abordados na literatura. Dentre os resultados foram obtidas 1.083 (82,8%) PE corretas e adequadas, 154 (11,8%) inadequadas e 52 (3,9%) incompletas. Em 18 (1,37%) prontuários, as PE eram inexistentes. Houve significância estatística (&#945;Estudio multicéntrico, transversal, realizado entre diciembre de 2009 a junio de 2010 y que tuvo como objetivo evaluar la calidad de Prescripción Enfermera (PE) en dos hospitales de enseñanza pública. La muestra consistió en 1307 PE y los datos fueron procesados a través de las pruebas estadísticas de G y chi-cuadrado. La determinación de la calidad de la PE se basó en los índices de clasificación reportados en la literatura. Entre los resultados obtenidos fueron 1.083 (82,8%) PE correctas y adecuadas, 154 (11,8%) inadecuadas y 52 (3,9%) incompletas. En 18 (1,37%) registros médicos, las PE no existían. Hubo significación estadística (&#945;A multicenter, cross-sectional study took place from December 2009 to June 2010 and aimed to assess the quality of Nursing Prescription (NP) in two public teaching hospitals. The sample consisted of 1,307 NP and data were processed using the G-test and chi-square. The determination of the quality of NP was based on the classification indices reported in literature. Among the results, 1,083 (82.8%) correct and appropriate NP, 154 (11.8%) inadequate NP and 52 (3.9%) incomplete NP were found. In 18 (1.37%) patient charts, the NP was nonexistent. There was statistic significance (&#945;<0.05) for incomplete and absent NP between the two hospitals (0.00), as well as inappropriate NP between age groups (0.03). It was concluded that, in the hospitals under study, the NP needs to be improved, both in terms of quantity and quality