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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Infecção pelos vírus C da hepatite em pacientes com cardiomiopatia dilatada idiopática

Submitted by Repositório Arca ([email protected]) on 2019-09-04T17:23:38Z No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Approved for entry into archive by Ana Maria Fiscina Sampaio ([email protected]) on 2019-09-11T12:57:54Z (GMT) No. of bitstreams: 2 Francisco Jose Farias Borges dos Reis Infecção...2006.pdf: 55549709 bytes, checksum: b37594dcd02af595fbb63a2e77a48a69 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5)Made available in DSpace on 2019-09-11T12:57:54Z (GMT). No. of bitstreams: 2 Francisco Jose Farias Borges dos Reis Infecção...2006.pdf: 55549709 bytes, checksum: b37594dcd02af595fbb63a2e77a48a69 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2006Fundação Bahiana para o Desenvolvimento das Ciências. Escola Bahiana de Medicina e Saúde Pública. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Centro de Pesquisas Gonçalo Moniz. Salvador, BA, Brasil.As cardiomiopatias dilatadas estão relacionadas a agentes etiológicos diversos que são responsáveis por lesões miocardicas que deterioram o músculo cardíaco. Uma das causas é a etiologia viral. Na literatura existem trabalhos que relacionam o VHC com a possível causa de CMDI. Um dos objetivos deste trabalho foi determinar se existe esta associação. Para tal, realizamos dois tipos de estudo. No primeiro, avaliamos a prevalência do VHC em todos os pacientes com CMDI em um ambulatório especializado em cardiomiopatias e comparamos com dois grupos controles, portadores de cardiomiopatia dilatada chagásica e isquêmica. O estudo encontrou 2,9 % (1/34) de sorologia positiva para o vírus da Hepatite C, sendo este paciente do grupo de cardiomiopatia dilatada idiopática. Um segundo estudo teve como objetivo pesquisar através de uma revisão sistemática da literatura a quantidade e qualidade dos trabalhos publicados sobre este assunto. Encontramos no total 62 trabalhos. Destes, após critério de seleção que incluíram os estudos de corte transversal ou caso controle, permaneceram 6 para a análise sistematizada. Para avaliar a qualidade destes últimos, utilizamos a padronização criada por Figueiredo e Tavares Neto. Os estudos foram classificados de baixa qualidade e dois únicos que mostraram associação positiva foram publicados no Japão e em um mesmo serviço. Concluímos que na nossa população estudada não encontramos associação entre o VHC em pacientes com CMDI e na literatura encontramos poucos estudos de qualidade metodológica descrevendo esta associação, portanto não sendo possível estabelecer forte evidência científica da relação do HCV e a CDMI.The dilated cardiomyopathy is related to diverse etiologic agents, responsible for myocardial injuries that spoil the cardiac muscle. One of the causes is the viral etiology. In the literature there are works that relate the Hepatitis C Virus (HCV) with the possible cause of idiopathic dilated cardiomyopathy (IDC). One of the objectives of this study was to determine if this association really exists. For such, we carried on through two types of study. In the first one, we evaluated the prevalence of the HCV in all the patients with IDC in a specialized clinic for Cardiomyopathies and compared with two control groups, patients with Chagas disease and ischemic dilated cardiomyopathy. This study found 2.9 % (1/34) of positive serology for the HCV, being this patient from the group of IDC. As a study, it had as its objective to search, through a systematic review of literature, the amount and quality of the works published on this subject. We found a total of 62 works. Of these, after selection criterion, that had included the studies of sectional prevalence and/or case studies, remained 6 for the systematic analysis. To evaluate the quality of these last ones, we used the standardization created by Figueiredo and Tavares-Neto. The studies had been classified as low quality and the only two of them that showed positive association, was published in Japan and in the same service. We concluded that in our studied population we did not find association between HCV and patients with IDC and in literature we found few studies of méthodologie quality describing this association, therefore it was not possible to establish strong scientific evidence of the relation of the HCV and IDC