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

Transpiração em espécie de grande porte na Floresta Nacional de Caxiuanã, Pará Transpiration in large size species in Caxiuanã National Forest, in the State of Pará, Brazil

Durante o experimento "O Impacto da Seca Prolongada nos Fluxos de Água e Dióxido de Carbono em uma Floresta Tropical Amazônica" (ESECAFLOR) realizou-se este trabalho. Trata-se de um subprojeto do Experimento de Grande escala da Biosfera-Atmosfera da Amazônia (LBA), localizado na Estação Científica Ferreira Pena, dentro da Floresta Nacional de Caxiuanã, Pará (1º 42- 30-- S; 51º 31-45-- W; 62 m altitude). A região tem floresta bem preservada, com dossel médio de 35 m. As espécies predominantes em terra-firme, são: Eschweilera coriacea (Mata-matá branco), Voucapoua americana (Acapu) e Protium pallidum (Breu Branco). Medidas foram realizadas entre 03 a 16 de dezembro de 2000 e 12 a 25 de janeiro de 2003, objetivando-se determinar a transpiração de dois exemplares de Eschweilera coriacea, mediante os efeitos da seca provocada. A área do ESECAFLOR compreende duas parcelas, cada uma com 1 ha, parcela A (controle) e parcela B (exclusão da chuva). Para o fluxo de seiva, o método foi o Balanço de Calor no Tronco, com sistema Sap Flow meter, P4.1; entre os períodos analisados, a transpiração média registrou aumento de 56% na árvore A237 (parcela A) e redução de 68% na árvore B381 (parcela B).<br>During the "Long-term impact of drought on water and carbon dioxide fluxes in Amazonian Tropical Rainforest Experiment" (ESECAFLOR), this study was carried out, which is a subproject of Large Scale Biosphere Atmosphere Experiment in Amazônia (LBA), located in the Ferreira Penna Scientific Station (FPSS) in the Caxiuanã National Forest (CNF) in Pará State (1º 42- 30-- S; 51º 31-45-- W; 62 m altitude). The region has a well-preserved forest, with canopy of 35 m. The predominate tree species in the landscape are Eschweilera coriacea (White Matá-matá), Voucapoua americana (Acapu) and Protium pallidum (White Pitch). Sap flow measurements were made in the wet season (03-16 December 2000 and 12-25 January 2003), to evidence the effect of long term induced drought, aiming to determinate the transpiration of Eschweilera coriacea. The ESECAFLOR site consists of two different areas with 1 ha each. Plot A (control) and Plot B (rainfall exclusion). The Trunk Heat Balance (THB) method was applied to sap flow measurements, by Sap Flow Meter P4.1 system. Between analysed periods, the mean transpiration of E. Coriacea increased 56% in the tree A237 (control plot) and decreased 68% in B381 (drought plot)