7 research outputs found
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
Mortalidade por câncer na região urbano-industrial da Baixada Santista, SP (Brasil)
INTRODUÇÃO: Visando a estudar a relação entre câncer e industrialização analisou-se a evolução da mortalidade por câncer da região na Baixada Santista, SP (Brasil), importante complexo industrial-portuário cujos municípios se agrupam em duas diferentes áreas quanto ao processo de industrialização. MÉTODOS: Selecionaram-se 8.546 óbitos por câncer (CID-9), de indivíduos do sexo masculino acima de dez anos de idade, residentes nos municípios da Baixada Santista, no período de 1980 a 1993. Calcularam-se as taxas de mortalidade padronizada pela população mundial e as respectivas razões entre as taxas para a região e seus estratos: Estrato I (complexo industrial-portuário - Santos, São Vicente, Cubatão e Guarujá), e Estrato II (não industrializado - Praia Grande, Mongaguá, Itanhaém e Peruíbe). RESULTADOS: A taxa anual média de mortalidade da Baixada Santista mostrou se alta (197,9/100.000). Houve diferença estatisticamente significante entre as taxas de mortalidade observadas para os Estratos I e II, respectivamente 209,2 e 146,7/100.000, com razão de 1,42 (IC 1,36 - 1,51). CONCLUSÕES: Supõe-se que a exposição ocupacional e ambiental a agentes químicos carcinogênicos relacionados ao processo produtivo do complexo industrial, vários deles já identificados, sejam fatores importantes na determinação da mortalidade por câncer. Nesse sentido outros estudos epidemiológicos são necessários para melhor caracterizar o excesso de mortalidade na área industrial da região estudada
Mortalidade por câncer na região urbano-industrial da Baixada Santista, SP (Brasil)
INTRODUÇÃO: Visando a estudar a relação entre câncer e industrialização analisou-se a evolução da mortalidade por câncer da região na Baixada Santista, SP (Brasil), importante complexo industrial-portuário cujos municípios se agrupam em duas diferentes áreas quanto ao processo de industrialização. MÉTODOS: Selecionaram-se 8.546 óbitos por câncer (CID-9), de indivíduos do sexo masculino acima de dez anos de idade, residentes nos municípios da Baixada Santista, no período de 1980 a 1993. Calcularam-se as taxas de mortalidade padronizada pela população mundial e as respectivas razões entre as taxas para a região e seus estratos: Estrato I (complexo industrial-portuário - Santos, São Vicente, Cubatão e Guarujá), e Estrato II (não industrializado - Praia Grande, Mongaguá, Itanhaém e Peruíbe). RESULTADOS: A taxa anual média de mortalidade da Baixada Santista mostrou se alta (197,9/100.000). Houve diferença estatisticamente significante entre as taxas de mortalidade observadas para os Estratos I e II, respectivamente 209,2 e 146,7/100.000, com razão de 1,42 (IC 1,36 - 1,51). CONCLUSÕES: Supõe-se que a exposição ocupacional e ambiental a agentes químicos carcinogênicos relacionados ao processo produtivo do complexo industrial, vários deles já identificados, sejam fatores importantes na determinação da mortalidade por câncer. Nesse sentido outros estudos epidemiológicos são necessários para melhor caracterizar o excesso de mortalidade na área industrial da região estudada