10 research outputs found

    Mechanisms of species coexistence and functional diversity of ant assemblages in forest and pasture habitats in southwestern Brazilian Amazon

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    In this study, we investigated the mechanisms behind species coexistence and the relationships between functional diversity and species richness in ant assemblages in both forest and pasture habitats in the southwestern Brazilian Amazon. We addressed the specific question: What is the primary mechanism for species coexistence in forest and pasture habitats? According to the identified mechanism in each habitat, we had the following alternative expectations: (i) niche partitioning – we expected to observe a linear positive relationship between functional diversity and species richness, indicating a complementary relationship; or (ii) niche filtering – a positive constant asymptotic relation between functional diversity and species richness, indicating a functional redundancy relationship. In total, we sampled 91 ant species, 82 species in a forest habitat and 16, in a pasture habitat. In the forest habitat we identified niche filtering as the structuring mechanism of the ant assemblage, but we were unable to identify a clear mechanism in the pasture habitat. Although the relationship between functional diversity and species richness was positive in both habitats, the relationship was weaker in the forest habitat, indicating a greater functional redundancy among the ant species in this habitat. Our results reinforce the divergence of species coexistence mechanisms and ant assemblage structures in both natural and human-modified habitats in the Southwestern Brazilian Amazon

    ABC<sub>2</sub>-SPH risk score for in-hospital mortality in COVID-19 patients:development, external validation and comparison with other available scores

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    Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients. Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.</p

    Pervasive gaps in Amazonian ecological research

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    Pervasive gaps in Amazonian ecological research

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    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

    Get PDF
    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

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    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

    Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry

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    The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients’ data were obtained through hospital records. Hospitals’ data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = − 0.37; 95% CI − 0.71 to − 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = − 0.40; 95% CI − 0.72 to − 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = − 0.59; 95% CI − 0.98 to − 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11–0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Medical School and University Hospital Universidade Federal de Minas Gerais Avenida Professor Alfredo Balena, 190, sala 246, Minas GeraisDepartament of Statistics Universidade Federal Do Rio Grande Do Sul, Rio Grande do SulCentro Universitário de Belo Horizonte (UniBH), Minas GeraisInstitute for Health Technology Assessment (IATS/ CNPq), Rio Grande do SulHospital Universitário de Santa Maria, Rio Grande do SulHospital UNIMED, Minas GeraisHospital São João de Deus, Minas GeraisHospital Regional Antônio Dias, Minas GeraisHospitais da Rede Mater Dei, Minas GeraisHospital Moinhos de Vento, Rio Grande do SulHospital Risoleta Tolentino Neves, Minas GeraisHospital Márcio Cunha, Minas GeraisHospital Júlia Kubistchek, Minas GeraisHospital Santa Rosália, Minas GeraisHospital João XXIII, Minas GeraisUniversidade Do Vale Do Taquari, Rio Grande do SulHospital Nossa Senhora da Conceição, Rio Grande do SulMedical School Universidade Estadual Paulista “Júlio de Mesquita Filho”, São PauloHospital SOS Cárdio, Santa CatarinaHospital Mãe de Deus, Rio Grande do SulHospital Universitário de Canoas, Rio Grande do SulHospital Santo Antônio, Minas GeraisUniversity Hospital Universidade Federal de Pernambuco, PernambucoFaculdade de Ciências Médicas de Minas Gerais University Hospital, Minas GeraisFaculdade de Saúde E Ecologia Humana (FASEH), Minas GeraisHospital São Lucas da Pontifícia Universidade Católica do Rio Grande do Sul (PUC-RS), Rio Grande do SulHospital das Clínicas da Faculdade de Medicina de Botucatu, São PauloUniversidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Minas GeraisHospital Tacchini, Rio Grande do SulCentro Universitário de Patos de Minas, Minas GeraisHospital Metropolitano Odilon Behrens, Minas GeraisHospital Santa Cruz, Rio Grande do SulHospital Eduardo de Menezes, Minas GeraisHospital Semper, Minas GeraisHospital Metropolitano Doutor Célio de Castro, Minas GeraisHospital Bruno Born, Rio Grande do SulUniversidade Federal do Rio Grande do Sul, Rio Grande do SulMedical School Universidade Estadual Paulista “Júlio de Mesquita Filho”, São PauloHospital das Clínicas da Faculdade de Medicina de Botucatu, São PauloCNPq: 465518/2014-1FAPEMIG: APQ-00208-2

    ABC-SPH risk score for in-hospital mortality in COVID-19 patients : development, external validation and comparison with other available scores

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    The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO/FiO ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19

    Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

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    International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora
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