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

Estratégias de análise em regime permanente para avaliação de confiabilidade composta de sistemas de energia elétrica

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2019.O método de simulação de Monte Carlo sequencial destaca-se como uma ferramenta adequada e flexível para a avaliação de confiabilidade de sistemas elétricos de potência. Esse método pode ser dividido em três estágios principais: seleção de estados, avaliação de estados e estimação de índices. Dentre esses três estágios, o estágio de avaliação de estados é conhecido como aquele que requer maior esforço computacional, principalmente quando um maior rigor é adotado na representação dos sistemas. Neste contexto, esta tese de doutorado tem como objetivo o desenvolvimento de estratégias de análise em regime permanente para a avaliação de confiabilidade composta de sistemas elétricos de potência. Desenvolvem-se metodologias que possibilitam evitar avaliações de estados via fluxo de potência e fluxo de potência ótimo no método de simulação de Monte Carlo sequencial, sem a perda do rigor adotado na representação dos sistemas e buscando manter a exatidão na estimação de índices de confiabilidade. Propõe-se assim uma modelagem do problema de mínimo e máximo carregamento considerando equações de rede linearizadas, com o fim de identificar patamares de carga limite acima e abaixo dos quais estados de sucesso são caracterizados. Essa modelagem é ainda explorada na otimização de parâmetros estocásticos promovida por um método de redução de variância baseado em conceitos de entropia cruzada. Além disso, uma extensão da modelagem supracitada é proposta considerando uma representação não linear para a rede, a aplicação de equações tensoriais para o problema de fluxo de potência, e uma abordagem preditor-corretor para a estimação de fatores de mínimo e máximo carregamento. Resultados numéricos são utilizados para realçar a efetividade das estratégias na redução do número de avaliações de estado necessárias à análise de confiabilidade de sistemas compostos.Abstract: The sequential Monte Carlo simulation method stands out as a suitable and flexible tool for the evaluation of the reliability of electrical power systems. This method can be divided in three main steps: state selection, state evaluation, and index estimation. Among these three stages, the state evaluation stage is recognized as the one that requires a significant part of the computational effort, particularly when major rigor is adopted in the system representation. In this context, this thesis aims to develop steady state analysis strategies to be applied in the evaluation of power system reliability. Methodologies are developed to avoid state evaluations via power flow and optimal power flow in the sequential Monte Carlo simulation method, without losing the rigor adopted in the system representation and targeting the maintenance of the accuracy in the index estimation. Therefore, a minimum and maximum loadability problem is modelled, considering linearized network equations, aiming at identifying loading thresholds above and below which success states are characterized. This modeling is further exploited in the optimization of stochastic parameters utilized in a variance reduction method based on cross entropy concepts. In addition, an extension of the aforementioned modeling is proposed, considering a nonlinear network representation, using the application of tensor equations at the power flow problem and a predictor-corrector approach to the estimation of minimum and maximum loading factors. Numerical results are used to improve the effectiveness of the strategies on reducing the number of state evaluations required to the analysis of composite system reliability