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

The emergence of multiple antimicrobial resistance in Vibrio cholerae isolated from gastroenteritis patients in Ceará, Brazil

Submitted by Sandra Infurna ([email protected]) on 2019-10-31T12:38:15Z No. of bitstreams: 1 ErnestoHofer_DaliaPrazeres_etal_IOC_1999.pdf: 67621 bytes, checksum: f914b6df4b14c58f22727914f5bf2a0e (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2019-10-31T12:46:09Z (GMT) No. of bitstreams: 1 ErnestoHofer_DaliaPrazeres_etal_IOC_1999.pdf: 67621 bytes, checksum: f914b6df4b14c58f22727914f5bf2a0e (MD5)Made available in DSpace on 2019-10-31T12:46:09Z (GMT). No. of bitstreams: 1 ErnestoHofer_DaliaPrazeres_etal_IOC_1999.pdf: 67621 bytes, checksum: f914b6df4b14c58f22727914f5bf2a0e (MD5) Previous issue date: 1999Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratórios de Zoonoses Bacterianas e de Enterobactérias. Rio de Janeiro, RJ, Brasil.Das 7058 amostras de Vibrio cholerae isoladas de pacientes com suspeita de síndrome coleriforme, no período de 1991 a 1993, no Estado do Ceará, foram detectadas duas com as características de múltipla resistência aos antimicrobianos (tetraciclina, ampicilina, eritromicina, sulfametoxazol-trimetoprima) e ao composto vibriostático O/129 (2,4-diamino-6,7d i i s o p ropilpteridina). Do ponto de vista bacteriológico uma amostra foi identificada como V. cholerae s o ro g rupo O:1, biotipo El Tor e sorovar Inaba e a outra, caracterizada como V. cho l e r a e so r og r upo O:22, classificada bioquimicamente no tipo II de Heiberg. Foi demonstrado que apenas na amostra do sorogrupo O:1, a multirresistência era codificada por um plasmídio, transferível por conjugação para Escherichia coli K12 e amostras sensíveis de V. cholerae O1 e não O1, numa freqüência entre 8x10- 2 a 5x10- 6. O plasmídio responsável pela multirresistência apresentou um peso molecular de 147 Kb, compatível com as descrições em outras partes do mundo.Of 7058 Vibrio cholerae strains recovered from patients suspected of cholera in the State of Ceará between December 1991 and September 1993, two were resistant to antimicrobials (Ampicillin, erythromycin, trimethoprim-sulfamethoxazole, tetracycline) and to vibriostatic agent O/129 (2,4-diamino-6,7-diisopropylpteridine). From the bacteriological standpoint, one strain was identified as V. cholerae serogroup O:1, biotype El Tor, serovar Inaba, and another as V. cholerae serogroup O:22, biochemically classified as Heiberg type II. It was shown that only in the serogroup O:1 strain, multiple resistance was encoded by a plasmid transferrable by conjugation to Escherichia coli K12 and a sensitive strains of V. cholerae O1 and non-O1, with at a frequency between 8 x 10(-2) and 5 x 10(-6). The plasmid, with a molecular weight of 147 Kb, encoded both multiple resistance to antimicrobials and the vibriostatic compound (O/129), compatible with descriptions reported in other parts of world