MAPEAMENTO PARTICIPATIVO DO USO DOS RECURSOS NATURAIS COMO FERRAMENTA DE GESTÃO PARTICIPATIVA: O CASO DA RESEX MARINHA ACAÚ-GOIANA PB/PE

O presente trabalho apresenta uma parte dos resultados obtidos por meio do trabalho desenvolvido junto ao PNUD/ICMBio Regional 6 - Cabedelo/ RESEX Acaú-Goiana PB/PE, realizado no ano de 2011/2012, que utilizou como metodologia o mapeamento participativo do uso dos recursos naturais bem como o uso do GPS e mapas com escala de 1:25000 e 1:50000 para registro em polígonos das áreas de uso dos recursos naturais. O desenvolvimento do trabalho contou com a participação dos pescadores (as) da Resex e os membros do grupo de trabalho (GT) para Formação do Conselho Deliberativo da RESEX. A metodologia proposta permite que por meio da participação efetiva, que os envolvidos demostrem seus conhecimentos sobre o território e sua cultura, fortalecendo tanto sua identidade, quanto sua autoestima, dignidade e minimizar ou excluir os riscos dos impactos indesejáveis, possibilitando-lhes reafirmaremse como protagonistas de suas histórias. A proposta metodológica consistiu em três momentos distintos: o primeiro, de coleta de dados em campo: o segundo, de geoprocessamento e georreferenciamento dos dados espaciais e tabulação de dados quantitativos e qualitativos relativos ao mapeamento participativo dos territórios e no terceiro o retorno das informações as populações tradicionais. O mapeamento foi realizado por meio de oficinas participativas onde se verificou as formas de uso do território e localização das áreas em crise dos recursos naturais e áreas de impactos negativos. Cada uma dessas etapas articula de modo diferente a expertise profissional e o conhecimento das populações tradicionais, sendo validadas por ambos

Updated cardiovascular prevention guideline of the Brazilian Society of Cardiology: 2019

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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