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

Integrated Modelling of Lake Pampulha: Assessing the Catchment Impact on Cyanobacteria Dynamics in the Lake

International audienceAmong the many pollutants loaded by the urban runoff, nitrogen and phosphorus are of particular concern for lakes which are especially vulnerable to nutrient enrichment because of the high water retention time. Eutrophic lakes are frequently affected by cyanobacteria blooms, including toxic species, which can be harmful to human health. To provide a tool for studying the impacts of catchment changes on the cyanobacteria dynamics in urban lakes, a modelling approach in which a hydrological model is connected to an ecological lake model is proposed and applied for Lake Pampulha, Brazil. The lake and its catchment were intensively monitored from October 2011 to June 2013. The hydrological model SWMM was used to simulate runoff volume and TSS, NO3, NH4 and P total . SWMM results were input into the lake ecological model DYRESM-CAEDYM, which was used to simulate cyanobacteria dynamics. The hydrological model showed a good performance for runoff simulation (Nash criterion between 0.70 and 0.88) and poor results for nutrient simulation. The lake model showed good predictive ability of the cyanobacteria dynamics (NMAE between 0.26 and 0.55). Results of the model and a sensibility analysis highlighted the link between cyanobacteria dynamics and the urban catchment

Modelagem da Lagoa da Pampulha: uma ferramenta para avaliar o impacto da bacia hidrográfica na dinâmica do fitoplâncton

RESUMO No processo de urbanização, o aumento na proporção de superfícies impermeabilizadas e as mudanças no uso do solo são responsáveis por maiores volumes e velocidades do escoamento superficial, refletindo em uma maior capacidade de arraste e em um maior aporte de nutrientes nos corpos d'água receptores. O objetivo deste trabalho foi implementar uma ferramenta matemática capaz de reproduzir o impacto de mudanças na bacia hidrográfica sobre a dinâmica do fitoplâncton em um lago urbano. Neste artigo são apresentados o desenvolvimento e os resultados de um modelo integrado constituído de um modelo hidrológico, para simular vazões escoadas em uma bacia hidrográfica, e de um modelo hidrodinâmico e ecológico, para simular a biomassa fitoplanctônica em um corpo d'água urbano. A Lagoa da Pampulha (Belo Horizonte, Minas Gerais), escolhida como estudo de caso, foi intensamente monitorada, assim como sua bacia de drenagem, entre outubro de 2011 e junho de 2013. Os dados obtidos foram utilizados para calibrar e validar ambos os modelos. Os resultados obtidos com o modelo hidrológico mostraram-se coerentes com as medidas realizadas em campo (o coeficiente de Nash variou entre 0,70 e 0,88). O modelo da lagoa representou corretamente a evolução da comunidade fitoplanctônica (erro médio absoluto normalizado: 0,25-0,42 e o coeficiente de Pearson: 0,82-0,89; p<0,0001). O monitoramento e a modelagem da lagoa mostraram que a proliferação de cianobactérias é bastante perturbada pelas desestratificações térmicas que ocorrem na lagoa em virtude de eventos meteorológicos. A ferramenta de simulação desenvolvida possui potencial para avaliar diferentes cenários de mudança das condições climáticas e das características da bacia, podendo auxiliar na gestão dos corpos d'água situados em meio urbano

Comparação da assistência em saúde mental em unidades básicas de saúde com ou sem equipe do Programa de Saúde da Família Comparison of mental health assistance in primary care settings with or without Family Health Program team

INTRODUÇÃO: O objetivo deste estudo foi comparar o perfil de assistência em saúde mental realizado por unidade básica de saúde (UBS) com equipe de Programa de Saúde da Família (PSF) e sem equipe de PSF. MÉTODO: Estudo observacional, avaliando pacientes encaminhados por UBS da área de abrangência de um serviço especializado de saúde mental no período de abril de 2003 a março de 2006. RESULTADOS: A UBS com equipe de PSF apresentou melhor padrão global de registros de dados, maior responsabilidade exclusiva do médico em suas referências ao nível especializado (p = 0,000), menor capacidade de retenção dos usuários na UBS (p = 0,099), maiores taxas de abandono de tratamento em nível secundário (p = 0,060) e menor percentual de contrarreferência pela equipe especializada (p = 0,028). A taxa de concordância diagnóstica global foi semelhante entre os dois modelos de UBS, com razoável nível de concordância (índice kappa de 44,5 e 43,0%, respectivamente, para UBS com e sem equipe PSF). CONCLUSÃO: A UBS com equipe de PSF não apresentou resultados compatíveis com o que seria de se esperar, em função de sua hipotética melhor qualidade de estrutura.<br>INTRODUCTION: The objective of this study was to compare the profile of mental health assistance provided at primary care units (PCUs) with and without a Family Health Program (FHP) team. METHOD: Observational study evaluating patients referred by PCUs located in the coverage area of a specialized mental health institution between April 2003 and March 2006. RESULTS: The PCU with a FHP team presented better global standards for data recording, higher exclusive participation of medical doctors on their referral to specialists (p = 0.000), lower capacity of patient retention (p = 0.099), higher rates of treatment dropout in secondary level (p = 0.060), and lower percentage of counter-referral by the specialized team (p = 0.028). The overall index of diagnostic agreement was similar for both types of PCU model, with a reasonable level of agreement (kappa index of 44.5 and 43.0%, respectively, for PCUs with and without a FHP team). CONCLUSION: The PCU with a FHP team did not present results compatible with what would be expected based on its hypothetically better quality