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

Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations

Submitted by Nuzia Santos ([email protected]) on 2016-02-19T13:11:37Z No. of bitstreams: 1 Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations..pdf: 501572 bytes, checksum: 45f5ed2fc0a7c2cb73e047a75457edae (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2016-02-19T13:37:09Z (GMT) No. of bitstreams: 1 Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations..pdf: 501572 bytes, checksum: 45f5ed2fc0a7c2cb73e047a75457edae (MD5)Made available in DSpace on 2016-02-19T13:37:09Z (GMT). No. of bitstreams: 1 Origin and dynamics of admixture in Brazilians and its effect on the pattern of deleterious mutations..pdf: 501572 bytes, checksum: 45f5ed2fc0a7c2cb73e047a75457edae (MD5) Previous issue date: 2015Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade Federal de Pelotas. Programa de Pós-Graduação em Epidemiologia. Pelotas, RS, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade Federal da Bahia. Instituto de Matemática. Departamento de Estatística. Salvador, Bahia, BrasilUniversidade Federal da Bahia. Instituto de Ciências da Saúde. Departamento de Ciências da Biointeração. Salvador, Bahia, BrasilUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilUniversity of Leicester. Department of Genetics. Leicester, United KingdomWashington University School of Medicine. Department of Molecular Microbiology. St. Louis, MO/University of California. Department of Medicine. San Diego, CAAsociación Benéfica Proyectos en Informática, Salud, Medicina y Agricultura. Biomedical Research Unit. Lima, PeruUniversidade Federal de Santa Catarina. Embriologia e Genética. Departamento de Biologia Celular. Florianópolis, SC, BrasilUniversidade Federal de Minas Gerais. Departamento de Estatística. Belo Horizonte, MG, BrasilUniversità di Ferrara. Dipartimento di Scienze della Vita e Biotecnologie. Ferrara, ItalyJohns Hopkins University. International Health. Bloomberg School of Public Health. Baltimore, MD, USA/Universidade Peruana Cayetano Heredia. Laboratorio de Investigación de Enfermedades Infecciosas. Lima, PeruUniversity of Toronto. Center for Addiction and Mental Health. Department of Psychiatry and Neuroscience Section. Toronto, ON, CanadaUniversidade Federal de Santa Catarina. Embriologia e Genética. Departamento de Biologia Celular. Florianópolis, SC, BrasilUniversidade Federal de Santa Catarina. Embriologia e Genética. Departamento de Biologia Celular. Florianópolis, SC, BrasilInnsbruck Medical University. Molecular and Clinical Pharmacology. Department of Medical Genetics. Division of Genetic Epidemiology. Innsbruck, AustriaFrederick National Laboratory for Cancer Research. Leidos Biomedical Research. Cancer Genomics Research Laboratory. Frederick, MDLondon School of Hygiene and Tropical Medicine. Faculty of Epidemiology. Department of Infectious Disease Epidemiology. London, United KingdomUniversidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, BrasilFundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, BrasilUniversidade de São Paulo. Instituto do Coração. São Paulo, SP, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, BrasilUniversidade Federal da Bahia. Instituto de Ciências da Saúde. Departamento de Ciências da Biointeração. Salvador, BA, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Laboratório de Computação Científica. Belo Horizonte, MG, Brasil.Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, Brasil.Universidade Federal de Pelotas. Programa de Pós-Graduação em Epidemiologia. Pelotas, RS, Brasil.Universidade Federal de Rio Grande do Sul. Centro Nacional de Supercomputação. Porto Alegre, RS, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Grupo de Genômica e Biologia Computacional. Belo Horizonte, MG, Brasil.Universidade Federal de Pelotas. Programa de Pós-Graduação em Epidemiologia. Pelotas, RS, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, Brasil.Fundação Oswaldo Cruz. Instituto de Pesquisa Rene Rachou. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Laboratório de Computação Científica. Belo Horizonte, MG, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.While South Americans are underrepresented in human genomic diversity studies, Brazil has been a classical model for population genetics studies on admixture.We present the results of the EPIGEN Brazil Initiative, the most comprehensive up-to-date genomic analysis of any Latin-American population. A population-based genomewide analysis of 6,487 individuals was performed in the context of worldwide genomic diversity to elucidate how ancestry, kinship, and inbreeding interact in three populations with different histories from the Northeast (African ancestry: 50%), Southeast, and South (both with European ancestry >70%) of Brazil. We showed that ancestry-positive assortative mating permeated Brazilian history. We traced European ancestry in the Southeast/South to a wider European/Middle Eastern region with respect to the Northeast, where ancestry seems restricted to Iberia. By developing an approximate Bayesian computation framework, we infer more recent European immigration to the Southeast/South than to the Northeast. Also, the observed low Native-American ancestry (6–8%) was mostly introduced in different regions of Brazil soon after the European Conquest. We broadened our understanding of the African diaspora, the major destination of which was Brazil, by revealing that Brazilians display two within-Africa ancestry components: one associated with non-Bantu/western Africans (more evident in the Northeast and African Americans) and one associated with Bantu/eastern Africans (more present in the Southeast/South). Furthermore, the whole-genome analysis of 30 individuals (42-fold deep coverage) shows that continental admixture rather than local post-Columbian history is the main and complex determinant of the individual amount of deleterious genotypes

Núcleos de Ensino da Unesp: artigos 2014: volume 4: os processos de interação na escola e educação inclusiva

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Resumos em andamento - Educação

Resumos em andamento - Educaçã

Resumos em andamento - Educação

Resumos em andamento - Educaçã

Giants of the Amazon:How does environmental variation drive the diversity patterns of large trees?

For more than three decades, major efforts in sampling and analyzing tree diversity in South America have focused almost exclusively on trees with stems of at least 10 and 2.5 cm diameter, showing highest species diversity in the wetter western and northern Amazon forests. By contrast, little attention has been paid to patterns and drivers of diversity in the largest canopy and emergent trees, which is surprising given these have dominant ecological functions. Here, we use a machine learning approach to quantify the importance of environmental factors and apply it to generate spatial predictions of the species diversity of all trees (dbh ≥ 10 cm) and for very large trees (dbh ≥ 70 cm) using data from 243 forest plots (108,450 trees and 2832 species) distributed across different forest types and biogeographic regions of the Brazilian Amazon. The diversity of large trees and of all trees was significantly associated with three environmental factors, but in contrasting ways across regions and forest types. Environmental variables associated with disturbances, for example, the lightning flash rate and wind speed, as well as the fraction of photosynthetically active radiation, tend to govern the diversity of large trees. Upland rainforests in the Guiana Shield and Roraima regions had a high diversity of large trees. By contrast, variables associated with resources tend to govern tree diversity in general. Places such as the province of Imeri and the northern portion of the province of Madeira stand out for their high diversity of species in general. Climatic and topographic stability and functional adaptation mechanisms promote ideal conditions for species diversity. Finally, we mapped general patterns of tree species diversity in the Brazilian Amazon, which differ substantially depending on size class