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

Psychometric properties of the Satisfaction with Life Scale in young Brazilian adults

The Satisfaction with Life Scale (SWLS) was originally proposed in the English language to measure the individual's overall perception in relation to life. The study aims to evaluate the psychometric properties of SWLS when applied to young adults and estimate the influence of demographic characteristics on life satisfaction, in a non-probabilistic sample of young adult individuals (18 to 35 years) of both sexes in Araraquara, São Paulo State, Brazil. We assessed the fit of SWLS to the data by confirmatory analysis, using the comparative fit index (CFI), Tucker-Lewis index (TLI), and standardized root mean square residual (SRMR). Reliability was estimated by the alpha ordinal coefficient and omega. Factor invariance was estimated by multigroup analysis, with CFI test of statistical difference (ΔCFI). Comparison of the mean scores on satisfaction with life according to sex, age, economic stratum, and employment status was performed with analysis of variance (ANOVA). Participation included 2,170 individuals (females: 67.8%; age: 22.09 years). The model's fit to the different samples was adequate (CFI = 0.981-0.998; TLI = 0.962-0.996; SRMR = 0.026-0.040; omega = 0.842-0.869; alpha = 0.862-0.889). Strict invariance was seen for the target variables. Life satisfaction was greater among individuals in higher economic strata. The data obtained with SWLS were valid, reliable, and invariant between samples with different sex, age, economic strata, and employment status. Life satisfaction was greater among individuals from higher economic strata and did not differ by sex, age, or employment status.publishedVersionPeer reviewe

σECFfactors of gram-positive bacteria

Photograph of a scene at the George Murrell Home

Maxillary incisor root morphology in patients with nonsyndromic tooth agenesis: a controlled cross-sectional pilot study

This study aimed to investigate the association between root morphology of maxillary incisors and nonsyndromic tooth agenesis in patients compared with a control group without agenesis. Methods: This controlled cross-sectional pilot study (1:4) was performed with a random sample of 335 records from Brazilian applicants for orthodontic treatment, paired by sex and age. Panoramic and periapical radiographs were analyzed to diagnose tooth agenesis and to assess root morphology. The agenesis group (n = 67) included patients with nonsyndromic tooth agenesis, and the control group (n = 268) included patients without tooth agenesis. The statistical analysis included the Student t test and z test, conditional logistic regression, and odds ratio estimates. Results: Occurrence of root morphological changes was significantly higher among patients with agenesis (P <0.05). Significant morphological changes (short, blunt, apically bent, and pipette-shaped roots) were found in the roots of remaining teeth when comparing agenesis and control groups (P <0.05). Patients with agenesis were more likely to show root morphological changes (odds ratio, 74.23; 95% confidence interval, 16.93-325.46; P <0.001). Conclusion: Patients with agenesis are more likely to present root morphological changes, which should be considered to minimize problems during orthodontic treatments157221221