A prática de gestão do esporte na perspectiva do lazer em uma instituição cultural

Objetivo: Analisar a gestão de um programa de esporte no âmbito do lazer e identificar o(s) perfil(is) de gestor(es) manifestados pelos profissionais de uma instituição cultural de ensino. Métodos: O trabalho se orientou pela pesquisa qualitativa, tendo como base um estudo de caráter descritivo e interpretativo, envolvendo: análise documental, entrevista e observação. Participaram do estudo quatro profissionais da instituição referida. Resultados: Após leitura dos dados identificamos um alinhamento entre a diretriz institucional e as perspectivas dos gestores em defesa de uma valorização do esporte enquanto oportunidade de aprendizagem e construção de novos comportamentos e hábitos no que corresponde à prática esportiva. Conclusão: Foi manifestada a dificuldade, por parte dos profissionais, de conjugar as várias ações de gestão dos distintos programas institucionais. Com relação ao(s) perfil(is) de gestor(es) no âmbito do esporte e lazer houve convergência entre os profissionais, atuantes em diferentes instâncias na instituição, ao defenderem uma perspectiva ampliada que valorizava a autoavaliação, saber ouvir e se valer de conhecimentos teóricos relativos à gestão

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