COMPARTIMENTAÇÃO MORFOPEDOLÓGICA DA MICRORREGIÃO DE QUIRINÓPOLIS, GOIÁS

O texto apresenta uma análise integrada dos atributos físicos da Microrregião de Quirinópolis, Goiás, para a determinação dos Compartimentos Morfopedológicos (CMP). A metodologia adotada foi de acordo com Castro e Salomão (2000), utilizando as características semelhantes da geomorfologia, pedologia, geologia e hipsometria para a delimitação dos compartimentos. Como resultado da classificação, foi possível identificar cinco CMP distintos

ANÁLISE GEOMORFOLOGICA PRELIMINAR DO CORRÉGO DO CERRADO NO MUNICÍPIO DE CANÁPOLIS/MG

Historicamente rios e cidades estiveram intrinsecamente ligados, foi através dos rios que impérios se desenvolveram. Entender a dinâmica do relevo e dos rios é importante para compreender os problemas ambientais e sociais, já que o relevo é fator preponderante para o plantio e/ou povoamento. Neste contexto, o presente trabalho tem por objetivo fazer um levantamento preliminar da geomorfologia do Córrego do Cerrado que drena o município de Canápolis/MG, por meio de revisões bibliográfica e campo

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