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

Epífitas vasculares ocorrem próximas de corpos d’água na Estação Ecológica da Serra das Araras

Vascular epiphytes, plants that evolve on other plant species, are considered bioindicators of environmental quality. Current study surveys epiphytes at the Serra das Araras ecological station, located in the savanna biome within the Amazon-Pantanal transition area, and evaluates the species’ potential as moisture bioindicators. Three transects were planned, parallel to the Camarinha stream and close to the Miranda stream, both within the Upper Paraguay Basin, with ten plots each, totaling 30 plots of 10 x 3 m. Variance analysis (Kuskall-Wallis) of abundance among transects, linear regression analysis with abundance and richness data were performed as a function of water body distance and Kernel density analysis. Six species of epiphytes were reported, with special reference to Phlebodium decumanum (Willd.) J.Sm. and Vanilla sp. Further, 19.6% of 143 phorophytes sampled were confirmed by epiphytes, of which 27 were Attalea speciosa Mart. ex Spreng. The transect close to the water bodies showed abundance with significant difference (p = 0.004) to transect 3. Fifteen of the 30 plots sampled had epiphytes and regression analysis showed a significant decrease for richness (p = 0.0003, R-adjusted = 0.372) and abundance (p = 0.042 R-adjusted = 0.139) as a function of the distance of the streams, following what has been reported in density analysis, with the highest concentration near the watercourses. Human intervention may have conditioned a low richness rate of epiphytes. The occurrence of these species is correlated with that of phorophyte Attalea speciosa established preferably in humid areas. Consequently, epiphytes may be bioindicators of the region´s environmental conditions.Ciências Ambientais (PPGCA) Universidade do Estado de Mato Grosso (UNEMAT)Programa de Pós-Graduação em Ciências Ambientais (PPGCA) Universidade do Estado de Mato Grosso (UNEMAT), Campus Cáceres (MT)Universidade do Estado de Mato Grosso (UNEMAT), MTPrograma de Mestrado e Doutorado em Ciências Ambientais (PPGCA) UNEMAT, MTBiologia Vegetal Universidade Estadual Júlio de Mesquita Filho, SPBiologia Vegetal Universidade Estadual Júlio de Mesquita Filho, S