EXTRAVERTED CHILDREN SWIM FASTER COMPARED TO INTROVERTED COUNTERPARTS REGARDLESS OF LIGHT AND SOUND NOISE LEVELS

Individual differences of personality are thought to influence motor performance. In terms of cortical arousal levels, because extraverts are infra-activated and introverts are hyper-activated, environment stimuli might enhance the impact of the extraversion trait on task performance. This study investigated the effect of light and sound noise on the swimming performance of extraverted and introverted children. 19 extraverts (12 boys, 7 girls) and 22 introverts (12 boys, 10 girls), ages 8.2 ± 0.9 years, adapted to water and swimming at intermediate levels. Participants performed two trials of the task (swimming 15 meters as fast as possible in crawl style) under two environment conditions: bright light/loud noise (A) and dim light/slight noise (B). Movements were filmed to allow calculation of time to complete the task and the stroke cycle. There was a significant effect for the group factor, with extraverts swimming faster than introverts. No effect was detected for the environment factor or the interaction group/environment. Regarding stroke cycle, no differences were found for group, environment or interaction. Although extraversion has not affected mechanical aspects of crawl style, compared to introverts, extraverts swan faster, showing a more effective process of reacting and executing movements in time-constraints tasks.Diferenças individuais de personalidade podem influenciar o desempenho motor. Em termos de ativação cortical, porque extrovertidos são infra-ativados e introvertidos hiperativados, os estímulos ambientais podem aumentar o efeito do traço de extroversão ao desempenhar tarefas. O presente estudo investigou o efeito da luminosidade e do ruído sonoro no desempenho natatório de crianças extrovertidas (19; 12 meninos, 7 meninas) e introvertidos (22; 12 meninos, 10 meninas), com idade de 8.2 ± 0.9 anos, adaptadas à água e com nível intermediário de natação. As crianças executaram duas tentativas da tarefa (nadar 15 metros o mais depressa possível em estilo crawl) sob duas condições ambientais: luz forte/ruído alto (A) e luz fraca/ruído baixo (B). Os movimentos foram filmados para cálculo de tempo para completar a tarefa e de ciclo de braçada. Houve efeito significativo para o fator grupo, com extrovertidos nadando mais rapidamente que introvertidos. Não houve efeito para o fator ambiente ou interação grupo/ambiente. Quanto ao ciclo de braçada, não houve diferenças para qualquer fator ou interação. Embora a extroversão não tenha afetado aspectos mecânicos do nado crawl, comparados aos introvertidos, os extrovertidos nadaram mais rapidamente, o que demonstra um processo mais efetivo para reagir e executar movimentos com restrições de tempo

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