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

Emetofobia: revisão crítica sobre um transtorno pouco estudado

INTRODUÇÃO: A emetofobia ou fobia de vômitos - que inclui o medo excessivo de vomitar ou de ver outras pessoas vomitando e pode ser desencadeado por estímulos internos e externos - é um transtorno mental complexo e pouco conhecido. OBJETIVO: Este estudo teve como objetivo levantar os conhecimentos disponíveis sobre diversos aspectos do quadro. MÉTODO: Revisão convencional da literatura dos últimos 30 anos utilizando como estratégia de busca as seguintes palavras-chave: emetofobia, emetofóbico, medo de vomitar, fobia de vomitar efobia de vômito. Foram incluídos artigos sobre epidemiologia, fenomenologia, diagnóstico diferencial e tratamento da emetofobia, assim como artigos referidos nestes. RESULTADOS: Não há dados de prevalência na população geral e pouco se sabe sobre a etiologia da emetofobia. A maioria dos estudos aponta predominância no sexo feminino, início precoce e curso crônico. Os comportamentos de esquiva podem impactar negativamente a vida ocupacional, social e familiar. Os principais diagnósticos diferenciais são: transtorno de pânico com agorafobia, fobia social, anorexia nervosa e transtorno obsessivo-compulsivo. Estudos de tratamento se resumem a relatos de casos e não há ensaios clínicos controlados, mas intervenções cognitivo-comportamentais parecem ser promissoras. CONCLUSÃO: Mais estudos são necessários para melhor compreensão sobre a epidemiologia, o quadro clínico, a etiologia, a classificação e o tratamento da emetofobia.INTRODUCTION: Emetophobia or fear of vomit - which includes an excessive fear of vomiting or seeing other people vomiting and can be triggered by internal and external stimuli -is a complex and fairly unknown disorder. OBJECTIVE: This study aimed at reviewing the current knowledge about this condition. METHOD: A conventional literature review of the previous 30 years, using as search strategy the following keywords: emetophobia, emetophobic, fear of vomiting, vomiting phobia, and phobia of vomit. All articles about the epidemiology, phenomenology, differential diagnosis and treatment of emetophobia were included, as well as articles cited in these ones. RESULTS: There are no available data on the prevalence in the general population and little is known about the etiology of emetophobia. Most studies describe predominance in females, early onset and chronic course. The avoidant behaviors can have a significant impact on occupational, social and family lives. The most important differential diagnoses are: panic disorder with agoraphobia, social phobia, anorexia nervosa and obsessive-compulsive disorder. Treatment studies are mostly case reports and no controlled clinical trials have been published. Cognitive-behavioral interventions, however, seem to be promising. CONCLUSION: More studies are needed for a better understanding of the epidemiology, clinical picture, etiology, classification and treatment of emetophobia