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

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

Linking environmental drivers with amphibian species diversity in ponds from subtropical grasslands

ABSTRACTAmphibian distribution patterns are known to be influenced by habitat diversity at breeding sites. Thus, breeding sites variability and how such variability influences anuran diversity is important. Here, we examine which characteristics at breeding sites are most influential on anuran diversity in grasslands associated with Araucaria forest, southern Brazil, especially in places at risk due to anthropic activities. We evaluate the associations between habitat heterogeneity and anuran species diversity in nine body of water from September 2008 to March 2010, in 12 field campaigns in which 16 species of anurans were found. Of the seven habitat descriptors we examined, water depth, pond surface area and distance to the nearest forest fragment explained 81% of total species diversity. Water depth, margin vegetation type, surface area and distance to the next body of water explained between 31-74% of the variance in abundance of nine of the 16 species. Thus, maintenance of body of water, of the vegetation along the water edge and natural forest fragments in the grasslands, along with fire control (used to renovation of pasture), are fundamentally important for the maintenance of anuran species diversity through the conservation of their breeding sites

Voltametria de Pulso Diferencial (VPD) em estado sólido de manchas de Cromatografia de Camada Delgada (CCD): um novo método de análise para fitoativos antioxidantes

A new electroanalytical method coupling TLC-DPV in solid state was developed for quantitative determination of phytoantioxidants with medicinal purpose, e.g. rosmarinic acid (RA) in samples of phytopharmaceuticals, e.g. rosemary (Rosmarinus officinalis L.). The method showed to be feasible, presenting linearity in concentrations ranging from 0.694 x 10-3 to 9.526 x 10-3 mol L-1 (r = 0.9945), good sensibility, selectivity, reproducibility, repeatability, agility and affordable cost. The concentrations of RA in different extracts of rosemary ranged from 0.05 to 0.52 (% w/w), presenting high recovery levels when compared to HPLC