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

Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees

Tecendo a rede assistencial em saúde mental com a ferramenta matricial Building the mental health care network with the matrix support tool

Objetivou-se analisar o matriciamento como ferramenta articuladora da rede de atenção em saúde mental. Trata-se de uma pesquisa de natureza qualitativa, realizada no Nordeste brasileiro, no período de março a abril de 2010. Utilizaram-se para a coleta das informações a entrevista semiestruturada e a observação sistemática. Como participantes da pesquisa, incluíram-se 47 profissionais de saúde da Estratégia Saúde da Família e dos Centros de Atenção Psicossocial, distribuídos em dois grupos. Os dados foram organizados e analisados pelos pressupostos da análise de conteúdo articulando o teórico com o empírico. Em cumprimento ao exigido, o estudo foi submetido à análise do Comitê de Ética em Pesquisa adequando-se às normas da pesquisa envolvendo seres humanos. Segundo os resultados evidenciaram, o apoio matricial é uma estratégia potente, pois possibilita a construção de um sistema articulado em rede no SUS, não limitado às fronteiras de um dado serviço. Interconectado por uma equipe de referência, que mobiliza diversos atores para lidar com o andamento do caso, o apoio matricial sinaliza os caminhos que viabilizam a conexão de redes de cuidados em saúde mental.<br>This study aimed to analyze the matrix support as an organizer tool of mental health care network. This is a qualitative survey, conducted in Brazilian Northeast, from March to April 2010. Systematic observations and semi-structured interviews were conducted with 47 health professionals from the Family Health Strategy and Psychosocial Care Centers. The collected information was organized and analyzed by content analysis. Pursuant to the requirements, the study was submitted to the Research Ethics Committee for adapting to the standards of research involving human beings. The results showed the matrix support as a powerful strategy since it enables the construction of a linkage in SUS network services, not limited to the borders of a specific service. Also, it is interconnected by a team of reference, which mobilizes different actors to deal with the progress of the case. Thus, matrix support signals pathways that enable the connection of mental health care networks