Radiação fotossinteticamente ativa incidente e Refletida acima e abaixo do dossel de floresta de Mata Atlântica em Coruripe, Alagoas

The study of solar radiation is important to understand the several physical, chemical and biological processes that occur in the biosphere, particularly in the forest. The objective of this study has been to evaluate the temporal evolution of incident and reflected Photosynthetic Active Radiation (PAR) above and below the canopy of the Mata Atlantica forest. The study has been conducted in a Private Reserve Natural Heritage, located in the Coruripe city, Alagoas, during the period from October 2009 to September 2010, based on the PAR (2, 13, 26 m) observations obtained at the micrometeorological station, installed on a 24 meters high tower (10° 17' 36"S, 36° 17' 24"W, 160 m asl). According to the results the incident and reflected PAR outside (PAR↓_Ext and PAR↑_Ext) and inside (PAR↑_Spf) forest follow the seasonality imposed by the apparent motion of the Sun. The higher PAR values occur during the dry season, exceeding 600 and 12 W m-2, and during the wet season these averages was less than 300 and 8.0 W m-2, influenced by cloudiness. At the beginning and ending of sunlight time PAR↑_Spf values near zero were measured. The opposite measurements of about 14 W m-2, around 12 h (November and December) were observed

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