6 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
Evidence of leptospiral exposure in neotropical primates rescued from illegal trade and a Zoo in Bahia, Brazil
Submitted by Ana Maria Fiscina Sampaio ([email protected]) on 2017-02-20T16:44:23Z
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Almeida DS Evidence of leptospiral....pdf: 743414 bytes, checksum: 89646aba56d57cb03b347888cf7ad8e6 (MD5)Approved for entry into archive by Ana Maria Fiscina Sampaio ([email protected]) on 2017-02-20T16:56:07Z (GMT) No. of bitstreams: 1
Almeida DS Evidence of leptospiral....pdf: 743414 bytes, checksum: 89646aba56d57cb03b347888cf7ad8e6 (MD5)Made available in DSpace on 2017-02-20T16:56:07Z (GMT). No. of bitstreams: 1
Almeida DS Evidence of leptospiral....pdf: 743414 bytes, checksum: 89646aba56d57cb03b347888cf7ad8e6 (MD5)
Previous issue date: 2016Universidade Federal da Bahia. Escola de Medicina Veterinária e Zootecnia. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, BrasilFundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, BrasilFundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil / Escola Baiana de Medicina e Saúde Pública. Salvador, BA, BrasilUniversidade Federal da Bahia. Escola de Medicina Veterinária e Zootecnia. Salvador, BA, BrasilParque Zoobotânico Getúlio Vargas. Salvador, BA, BrasilCentro de Triagem de Animais Silvestres Chico Mendes (CETAS). Salvador, BA, BrasilFundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Faculdade de Medicina. Departamento de Patologia e Medicina Legal. Salvador, BA, BrasilUniversidade Federal da Bahia. Escola de Medicina Veterinária e Zootecnia. Salvador, BA, BrasilPoucos estudos compararam a
soroprevalência de aglutininas antileptospira com a demonstração
de excreção urinária de leptospiras ou evidência
de infecção ativa em primatas não humanos. A população
estudada consistiu em 58 animais, sendo 42 primatas do
Parque Zoobotânico Getúlio Vargas, Bahia, Brasil. O estudo
avaliou ainda 16 primatas (Cebus sp.) resgatados do tráfico
ilegal e abrigados no Centro de Triagem de Animais Silvestres
Chico Mendes, Salvador, Bahia, Brasil. A soroprevalência
de anticorpos antileptospira foi baixa (2%) nos animais
do Zoológico. Uma taxa mais elevada (31%) foi observada
nos animais resgatados do tráfico ilegal. Mesmo que todas
as amostras de sangue e urina tenham sido negativas para
DNA de leptospiras, a alta frequência de evidência de exposição
nos animais de origem selvagem indicam o risco potencial
da adoção de primatas como animais de estimação.Few studies have compared the seroprevalence of antileptospiral agglutinins with the
demonstration of urinary shedding of leptospires or evidence of active infection in the
bloodstreams of non-human primates. The study population consists of 58 animals, including
d 42 monkeys from the Zoological Park of Salvador (Parque Zoobotânico Getúlio
Vargas), Bahia, Brazil. The study also evaluated 16 primates (Cebus sp.) rescued from illegal
trade that were housed in the Wildlife Rehabilitation Center of Salvador (CETAS), Bahia,
Brazil. The seroprevalence of antileptospiral antibodies was low (2%) in the animals from
the Zoo. A higher rate (31%) was observed among the animals that were rescued from
illegal trade in the state of Bahia. Even if all the blood and urine samples were negative for
leptospiral DNA fragments, the high frequency of serological evidence of exposure suggests
a potential risk of leptospirosis transmission when keeping these animals as pets
Ionic imbalance and lack of effect of adjuvant treatment with methylene blue in the hamster model of leptospirosis
Leptospirosis in humans usually involves hypokalaemia and hypomagnesaemia and the putative mechanism underlying such ionic imbalances may be related to nitric oxide (NO) production. We previously demonstrated the correlation between serum levels of NO and the severity of renal disease in patients with severe leptospirosis. Methylene blue inhibits soluble guanylyl cyclase (downstream of the action of any NO synthase isoforms) and was recently reported to have beneficial effects on clinical and experimental sepsis. We investigated the occurrence of serum ionic changes in experimental leptospirosis at various time points (4, 8, 16 and 28 days) in a hamster model. We also determined the effect of methylene blue treatment when administered as an adjuvant therapy, combined with late initiation of standard antibiotic (ampicillin) treatment. Hypokalaemia was not reproduced in this model: all of the groups developed increased levels of serum potassium (K). Furthermore, hypermagnesaemia, rather than magnesium (Mg) depletion, was observed in this hamster model of acute infection. These findings may be associated with an accelerated progression to acute renal failure. Adjuvant treatment with methylene blue had no effect on survival or serum Mg and K levels during acute-phase leptospirosis in hamsters