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

Reliability of information on people with disabilities gathered by community health workers in highly consanguineous communities of Northeastern Brazil

Abstract Background In Brazil, community health workers have gathered monthly information on people with disabilities to maintain the Primary Care Information System since 1998; however, few studies have used this database for scientific or public health policy purposes. Objectives This study aimed to evaluate the reliability of information on people with disabilities gathered by community health workers in primary care services. Method This was a cross-sectional population-based study conducted in two highly consanguineous communities, involving a population of 18,458 inhabitants in Northeastern Brazil. To study the prevalence of people with disabilities, estimations performed by health workers were compared with those obtained by researchers who interviewed 15.6% of the total population. To study the agreement of the information, data on 106 people with disabilities completed independently by researchers and health workers were compared to evaluate the degree of agreement for 28 variables analysed. Kappa statistics (κ) were used to calculate the inter-rater agreement. Results The prevalence of disability estimated by community health workers was 3.01 and 2.00% for city A and B, respectively, while the percentages obtained by researchers were 6.72 and 5.65%, respectively, showing an underestimation of prevalence according to community health workers. The Kappa index value obtained for all data analysed (2,589 items excluding losses) was 0.808 (p < 0.01), indicating an almost perfect consistency of information collected by health workers compared to by researchers. Conclusion Community health workers collected information with a high degree of reliability, although the identification of the prevalence of disabled individuals was potentially impaired due to the work process

É o bicho

Apresenta animação que trabalha as estruturas aditivas, utilizando-se situações que estão relacionadas á temática de extinção de animais da região Amazônica. As atividades encontradas na animação possibilita ao usuário organizar as estruturas aditivas,refletir e avaliar o pensamento matemáticoComponente Curricular::Ensino Fundamental::Séries Iniciais::Matemátic

É o bicho

Componente Curricular::Ensino Fundamental::Séries Iniciais::MatemáticaApresenta animação que trabalha as estruturas aditivas, utilizando-se situações que estão relacionadas á temática de extinção de animais da região Amazônica. As atividades encontradas na animação possibilita ao usuário organizar as estruturas aditivas,refletir e avaliar o pensamento matemátic