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 perfusion status on central and mixed venous oxygen saturation in septic patients

Abstract Background and objectives Although there is controversy regarding the role of venous oxygen saturation in the initial resuscitation of septic patients with hypoperfusion these markers are still widely used. This study aimed to evaluate the correlation and concordance between central (SvcO2) and mixed (SvO2) oxygen saturation in septic shock patients with or without hypoperfusion in addition to the impact of these differences in patient conduction. Methods Patients with septic shock were monitored with pulmonary artery catheter and the following subgroups of hypoperfusion were analyzed: 1) lactate > 28 mg.dL-1; 2) base excess ≤ -5 mmol.L-1; 3) venoarterial CO2 gradient > 6 mmHg; 4) SvO2 28 mg.dL-1 and SvO2 28 mg.dL-1 and SvcO2 < 75%. Results Seventy-seven samples from 24 patients were included. There was only a moderate correlation between SvO2 and SvcO2 (r = 0.72, p = 0.0001) and there was no good concordance between these variables (7.35% bias and 95% concordance limits of -3.0% to 17.7%). Subgroup analysis according to the presence of hypoperfusion showed no differences in concordance between variables. There was discordance regarding clinical management in 13.8% (n = 9) of the cases. Conclusions There is a moderate correlation between SvO2 and SvcO2; however, the concordance between them is inadequate. It was not possible to demonstrate that the presence of hypoperfusion alters the concordance between SvO2 and SvcO2. The use of SvO2 instead of SvcO2 may lead to changes in clinical management in a small but clinically relevant portion of patients

Accuracy of different methods for blood glucose measurement in critically ill patients

CONTEXT AND OBJECTIVE: Although glucometers have not been validated for intensive care units, they are regularly used. The aim of this study was to compare and assess the accuracy and clinical agreement of arterial glucose concentration obtained using colorimetry (Agluc-lab), capillary (Cgluc-strip) and arterial (Agluc-strip) glucose concentration obtained using glucometry and central venous glucose concentration obtained using colorimetry (Vgluc-lab). DESIGN AND SETTING: Cross-sectional study in a university hospital. METHOD: Forty patients with septic shock and stable individuals without infection were included. The correlations between measurements were assessed both in the full sample and in subgroups using noradrenalin and presenting signs of tissue hypoperfusion. RESULTS: Cgluc-strip showed the poorest correlation (r = 0.8289) and agreement (-9.87 ± 31.76). It exceeded the limits of acceptable variation of the Clinical and Laboratory Standards Institute in 23.7% of the cases, and was higher than Agluc-lab in 90% of the measurements. Agluc-strip showed the best correlation (r = 0.9406), with agreement of -6.75 ± 19.07 and significant variation in 7.9%. For Vgluc-lab, r = 0.8549, with agreement of -4.20 ± 28.37 and significant variation in 15.7%. Significant variation was more frequent in patients on noradrenalin (36.4% versus 6.3%; P = 0.03) but not in the subgroup with hypoperfusion. There was discordance regarding clinical management in 25%, 22% and 15% of the cases for Cgluc-strip, Vgluc-lab and Agluc-strip, respectively. CONCLUSION: Cgluc-strip should be avoided, particularly if noradrenalin is being used. This method usually overestimates the true glucose levels and gives rise to management errors. CLINICAL TRIAL REGISTRATION: ACTRN12608000513314 (registered as an observational, cross-sectional study)

In Case of Fire, Escape or Die: A Trait-Based Approach for Identifying Animal Species Threatened by Fire

Recent studies have argued that changes in fire regimes in the 21st century are posing a major threat to global biodiversity. In this scenario, incorporating species’ physiological, ecological, and evolutionary traits with their local fire exposure might facilitate accurate identification of species most at risk from fire. Here, we developed a framework for identifying the animal species most vulnerable to extinction from fire-induced stress in the Brazilian savanna. The proposed framework addresses vulnerability from two components: (1) exposure, which refers to the frequency, extent, and magnitude to which a system or species experiences fire, and (2) sensitivity, which reflects how much species are affected by fire. Sensitivity is based on biological, physiological, and behavioral traits that can influence animals’ mortality “during” and “after” fire. We generated a Fire Vulnerability Index (FVI) that can be used to group species into four categories, ranging from extremely vulnerable (highly sensible species in highly exposed areas), to least vulnerable (low-sensitivity species in less exposed areas). We highlight the urgent need to broaden fire vulnerability assessment methods and introduce a new approach considering biological traits that contribute significantly to a species’ sensitivity alongside regional/local fire exposure