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

Influence of sociodemographic and emotional factors on the relationship between self-compassion and perceived stress among men residing in Brazil during the COVID-19 pandemic

The analysis of sociodemographic and emotional factors is essential to understanding how men perceive stress and practice self-compassion. In health crises, this problem becomes an emergency for public health. This study aimed to analyze the influence of sociodemographic and emotional factors on the relationship between self-compassion and the perceived stress of men residing in Brazil during the COVID-19 pandemic. This is a nationwide cross-sectional study carried out between June and December 2020 with 1006 men who completed a semi-structured electronic questionnaire. Data were collected using the snowball technique. Perceived stress was measured by the Perceived Stress Scale (PSS-14), and self-compassion was assessed using the Self-Compassion Scale. Most men had low self-compassion (51.5%; n = 516) and a moderate level of perceived stress (60.9%; n = 613), while 15.9% (n = 170) had a high level of stress. The prevalence of men in the combined situation of low self-compassion and high perceived stress was 39.4% (n = 334). Living with friends had a higher prevalence of low self-compassion and high perceived stress. The prevalence of common mental disorders was high (54.3%). Men with low levels of self-compassion reported higher levels of perceived stress; however, this association was moderated by emotional and sociodemographic variables. These findings highlight the importance of considering individual and contextual factors in public policies promoting men’s mental health.info:eu-repo/semantics/publishedVersio