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

Enterotoxigenic and nontoxigenic Bacteroides fragilis strains isolated in Brazil

Submitted by Sandra Infurna ([email protected]) on 2018-07-17T17:30:30Z No. of bitstreams: 1 ilanat_balassiano_etal_IOC_2008.pdf: 315376 bytes, checksum: e702ff045eb09dfe8cbf523d59202094 (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2018-07-17T17:52:59Z (GMT) No. of bitstreams: 1 ilanat_balassiano_etal_IOC_2008.pdf: 315376 bytes, checksum: e702ff045eb09dfe8cbf523d59202094 (MD5)Made available in DSpace on 2018-07-17T17:52:59Z (GMT). No. of bitstreams: 1 ilanat_balassiano_etal_IOC_2008.pdf: 315376 bytes, checksum: e702ff045eb09dfe8cbf523d59202094 (MD5) Previous issue date: 2008Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Mèdica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Mèdica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Mèdica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instittuto Oswaldo Cruz. Laboratório de Zoonoses Bacterianas. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.Universidade Federal Fluminense. Faculdade de Farmácia. Departamento de Tecnologia Farmacêutica. Niterói, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Zoonoses Bacterianas. Rio de Janeiro, RJ. Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Zoonoses Bacterianas. Rio de Janeiro, RJ. Brasil.Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo de Góes. Departamento de Microbiologia Médica. Laboratório de Biologia de Anaeróbios. Rio de Janeiro, RJ, Brasil.The presence of enterotoxigenic Bacteroides fragilis and nontoxigenic B. fragilis (NTBF) among 109 strains isolated from 1980-2008 in Brazil were investigated by PCR. One strain, representing 0.9% of the total analyzed strains, harbored the bft gene which was identified as bft-1 isoform based on PCR-RFLP and sequencing. Fortynine strains (44.9%) exhibited the NTBF pattern III which possesses the flanking region required for pathogenicity island acquisition in which the bft gene is codified. These data reinforce the potential of B. fragilis as an emerging enteropathogen in our country

Enterotoxigenic and nontoxigenic Bacteroides fragilis strains isolated in Brazil

The presence of enterotoxigenic Bacteroides fragilis and nontoxigenic B. fragilis (NTBF) among 109 strains isolated from 1980-2008 in Brazil were investigated by PCR. One strain, representing 0.9% of the total analyzed strains, harbored the bft gene which was identified as bft-1 isoform based on PCR-RFLP and sequencing. Forty-nine strains (44.9%) exhibited the NTBF pattern III which possesses the flanking region required for pathogenicity island acquisition in which the bftgene is codified. These data reinforce the potential of B. fragilis as an emerging enteropathogen in our country

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans—anteaters, sloths, and armadillos—have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard to sloth species, Bradypus variegatus has the most records (n = 962), and Bradypus pygmaeus has the fewest (n = 12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans data set. Please cite this data paper when using its data in publications. We also request that researchers and teachers inform us of how they are using these data