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

Draft genome sequence of Acinetobacter pittii ST643 shared by cystic fibrosis patients

Submitted by Sandra Infurna ([email protected]) on 2017-02-23T17:08:28Z No. of bitstreams: 1 anapaula_assef_etal_IOC_2016.pdf: 198814 bytes, checksum: 385ec48436670499c42fbb702ebfec20 (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2017-02-23T17:17:41Z (GMT) No. of bitstreams: 1 anapaula_assef_etal_IOC_2016.pdf: 198814 bytes, checksum: 385ec48436670499c42fbb702ebfec20 (MD5)Made available in DSpace on 2017-02-23T17:17:41Z (GMT). No. of bitstreams: 1 anapaula_assef_etal_IOC_2016.pdf: 198814 bytes, checksum: 385ec48436670499c42fbb702ebfec20 (MD5) Previous issue date: 2016Universidade do Estado do Rio de Janeiro. Faculdade de Ciências Médicas. Departamento de Microbiologia, Imunologia e Parasitologia. Rio de Janeiro, RJ, Brasil.Universidade do Estado do Rio de Janeiro. Faculdade de Ciências Médicas. Departamento de Microbiologia, Imunologia e Parasitologia. Rio de Janeiro, RJ, Brasil.Universidade do Estado do Rio de Janeiro. Faculdade de Ciências Médicas. Departamento de Microbiologia, Imunologia e Parasitologia. Rio de Janeiro, RJ, Brasil.Universidade do Estado do Rio de Janeiro. Faculdade de Ciências Médicas. Departamento de Microbiologia, Imunologia e Parasitologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Pesquisa em Infecção Hospitalar. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional da Saúde da Mulher, da Criança e do Adolescente Fernandes Figueira. Departamento de Pneumologia Pediátrica. Rio de Janeiro, RJ, Brasil.Universidade do Estado do Rio de Janeiro. Instituto de Biologia Roberto Alcântara Gomes. Departamento de Bioquímica. Rio de Janeiro, RJ, Brasil.Universidade do Estado do Rio de Janeiro. Faculdade de Ciências Médicas. Departamento de Microbiologia, Imunologia e Parasitologia. Rio de Janeiro, RJ, Brasil.Acinetobacter pittii has emerged as an important hospital pathogen that is associated with outbreaks and drug resistance. In cystic fibrosis (CF) patients, the detection of Acinetobacter spp. is rare; however, we isolated the A. pittii sequence type ST643 in several Brazilian CF patients treated in the same centre. The current study describes the draft genome of A. pittii ST643

Draft genome sequences of four Achromobacter ruhlandii strains isolated from cystic fibrosis patients

Achromobacter species are being increasingly isolated from the respiratory tract of cystic fibrosis patients. Recent reports indicate that Achromobacter ruhlandii is a potential human pathogen in cystic fibrosis-related infections. Here we report the draft genome of four A. ruhlandii strains isolated from cystic fibrosis patients in Brazil. This report describes A. ruhlandii as a potential opportunistic pathogen in cystic fibrosis and provides a framework to for additional enquires into potential virulence factors and resistance mechanisms within this species