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

Leitura e interpretação de enunciados de problemas escolares de matemática por alunos do ensino fundamental regular e educação de jovens e adultos (EJA)

Neste trabalho analisamos a compreensão leitora de alunos do ensino fundamental (na estrutura regular e no sistema de Educação de Jovens e Adultos - EJA) de enunciados de problemas escolares de Matemática necessária à resolução destes. Os dados para a análise foram coletados por meio de entrevistas realizadas com vinte alunos do ensino fundamental regular (dez alunos da 5.ª série e dez da 8.ª) e dez do EJA (cinco concluintes da Fase I e cinco concluintes da Fase II do ensino fundamental). Nas entrevistas, pautadas no método clínico-crítico de Jean Piaget, foram apresentados aos participantes quatro problemas adaptados de duas das coleções didáticas mais utilizadas pelos professores da região em sala de aula. A análise dos dados foi fundamentada na noção bakthiniana de gênero discursivo e em autores que focalizam aspectos cognitivos da leitura e interpretação de textos como Solé e Kleiman, a comunicação e a linguagem na prática educativa em matemática como Gómez-Granell, e a resolução de problemas como Bacquet e Medeiros. Os resultados mostraram que os participantes apresentavam falhas em sua compreensão leitora conforme os pontos de vista linguístico e matemático e pouca familiaridade com o gênero discursivo "enunciado de problemas matemáticos"; também não tinham noção precisa do significado de resolver um problema e apresentavam dificuldade em reter e manter o controle adequado das informações essenciais dos enunciados