La geografía física y esférica del Paraguay y Misiones Guaraníes

Motivo de íntima satisfacción será para los investigadores del pasado americano, todo esfuerzo que tienda a profundizar el conocimiento, de los diferentes hechos o cuestiones que, como principios fijos, perfectamente caracterizados, constituyen á la novísima y sugestiva ciencia americanista, y, con mayor razón en este caso, por tratarse de un justiciero homenaje á la labor seria de don Félix de Azara; el distinguido geógrafo español, cuyos rasgos más salientes lo constituyen, su constante, activa y provechosa actuación en el secular pleito de límites entre España y Portugal; viajes y estudios que el fracaso de la demar cación le permitió hacer, con indiscutible beneficio para la dilatada comarca que comprende buena parte de los dominios de tres nacionalidades. Por ello, el Uruguay, el Paraguay y la Argentina, le guardan gratitud

Apuntamientos para la Historia Natural de los cuadrúpedos del Paraguay y Rio de la Plata

Copia digital : Biblioteca Virtual de Patrimonio Bibliográfico (Ministerio de Educación, Cultura y Deporte

Memorias sobre el estado rural del Rio de la Plata en 1801, demarcación de límites entre el Brasil y el Paraguay á últimos del siglo XVIII,e informes sobre varios particulares de la América meridional española

Tít. de cub.: Memorias póstumas sobre asuntos del Rio de la Plata y del ParaguaySignaturizadoAntep.Port. con escenas xil.H. de lám.: "V. Rodes d., Amills g.

PARAGUAY. Mapas generales (1793). 1:8929187

Firmado y rubricado por el autorEscala gráfica de 20 leguas marítimas [= 13,4 cm] y de 20 leguas paraguayas [=10,2 cm]Orografía representada mediante perfilesAbundante toponimia de los accidentes geográficos y localización de las tribus indígenas de la zonaNotas explicativas bajo el títuloManuscrito lavado en verde y ocreMontado en cartulinaCopia digital. Madrid : Ministerio de Cultura. Dirección General del Libro, Archivos y Bibliotecas, 201

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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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