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

Condutas de risco à saúde e indicadores de estresse psicossocial em adolescentes estudantes do Ensino Médio Health risk behaviors and psychosocial distress indicators in high school students

O objetivo deste artigo foi analisar a associação entre condutas de risco à saúde (tabagismo, consumo de bebidas alcoólicas e uso de drogas) e indicadores de estresse psicossocial em adolescentes estudantes do ensino médio. A pesquisa foi realizada com amostra constituída por 4.210 adolescentes estudantes de escolas públicas do Estado de Pernambuco, Brasil. O Global School-based Student Health Survey foi usado para coletar dados pessoais (demográficos e socioeconômicos) e comportamentais, e para obter medidas dos indicadores de estresse psicossocial (variáveis desfecho). Foram observadas prevalências de tristeza, sentimento de solidão, pensamento de suicídio, dificuldade para dormir devido à preocupação e planos de suicídio. Tabagismo, consumo de bebidas alcoólicas e uso de drogas foi relatado, respectivamente, por 7,7%, 30,3% e 6,9%. As prevalências de indicadores de estresse psicossocial foram maiores entre as moças, e as prevalências de exposição a condutas de risco à saúde foram maiores entre os rapazes. Concluiu-se com o estudo que o uso de drogas está diretamente associado ao pensamento e plano de suicídio e, entre as moças, o consumo de bebidas alcoólicas foi um fator associado ao estresse psicossocial.<br>The purpose of this article was to examine the association between health risk behaviors (tobacco, alcohol, and drug use) and psychosocial distress indicators among high school students. The sample consisted of 4,210 adolescent students from public schools in Pernambuco State, Brazil. The Global School-based Student Health Survey was used to collect personal (demographic and socioeconomic) and behavioral data and to obtain measures of psychosocial distress indicators (outcome variables). Prevalence rates were observed for sadness, loneliness, suicidal ideation, sleeplessness due to worries, and suicidal planning. Self-reported prevalence rates for tobacco, alcohol, and drug use were 7.7%, 30.3%, and 6.9%. Psychosocial distress was more prevalent among girls, while health risk behaviors were more common among boys. The study concludes that drug use is directly associated with suicidal ideation and planning, and that among girls, alcohol consumption was associated with psychosocial distress