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

SEVERE CALORIE RESTRICTION REDUCES CARDIOMETABOLIC RISK FACTORS AND PROTECTS RAT HEARTS FROM ISCHEMIA/REPERFUSION INJURY

Background and Aims: Recent studies have proposed that if a severe caloric restriction (SCR) is initiated at the earliest period of postnatal life, it can lead to beneficial cardiac adaptations later on. We investigated the effects of SCR in Wistar rats from birth to adult age on risk factors for cardiac diseases (CD), as well as cardiac function, redox status and HSP72 content in response to ischemia/reperfusion (I/R) injury. Methods and Results: From birth to the age of 3 months, CR50 rats were fed 50% of the food that the ad libitum group (AL) was fed. Food intake was assessed daily and body weight were assessed weekly. In the last week of the SCR protocol, systolic blood pressure and heart rate were measured and the double product index was calculated. Also, oral glucose and intraperitoneal insulin tolerance tests were performed. Thereafter, rats were decapitated, visceral fat was weighed, and blood and hearts were harvested for biochemical, functional, tissue redox status and western blot analyzes. Compared to AL, CR50 rats had reduced the main risk factors for CD. Moreover, the FR50 rats showed increased cardiac function both at baseline conditions (45% > AL rats) and during the post-ischemic period (60% > AL rats) which may be explained by a decreased cardiac oxidative stress and increased HSP72 content. Conclusion: SCR from birth to adult age reduced risk factors for CD, increased basal cardiac function and protected hearts from the I/R, possibly by a mechanism involving ROS

The effect of different water immersion temperatures on post-exercise parasympathetic reactivation.

PURPOSE: We evaluated the effect of different water immersion (WI) temperatures on post-exercise cardiac parasympathetic reactivation. METHODS: Eight young, physically active men participated in four experimental conditions composed of resting (REST), exercise session (resistance and endurance exercises), post-exercise recovery strategies, including 15 min of WI at 15°C (CWI), 28°C (TWI), 38°C (HWI) or control (CTRL, seated at room temperature), followed by passive resting. The following indices were assessed before and during WI, 30 min post-WI and 4 hours post-exercise: mean R-R (mR-R), the natural logarithm (ln) of the square root of the mean of the sum of the squares of differences between adjacent normal R-R (ln rMSSD) and the ln of instantaneous beat-to-beat variability (ln SD1). RESULTS: The results showed that during WI mRR was reduced for CTRL, TWI and HWI versus REST, and ln rMSSD and ln SD1 were reduced for TWI and HWI versus REST. During post-WI, mRR, ln rMSSD and ln SD1 were reduced for HWI versus REST, and mRR values for CWI were higher versus CTRL. Four hours post exercise, mRR was reduced for HWI versus REST, although no difference was observed among conditions. CONCLUSIONS: We conclude that CWI accelerates, while HWI blunts post-exercise parasympathetic reactivation, but these recovery strategies are short-lasting and not evident 4 hours after the exercise session

Mean R-R (panel A), ln rMSSD (panel B), ln SD1 (panel C) and SD2 (panel D) at rest (REST), during exercise (EXE), during post-exercise water immersion (WI), during post-water immersion (POST-WI), and during post-recovery period (POST-REC) for no immersion (CTRL, ο), cold water immersion (CWI, ▪), temperate water immersion (TWI, Δ) and hot water immersion (HWI, ▾).

<p>^a p<0.05 vs REST for all recovery strategies; * p<0.05 vs rest in CTRL; ^δ p<0.05 vs rest in TWI; ^Ω p<0.05 vs rest in HWI; ^# p<0.05 CWI vs CTRL; ^$ p<0.05 HWI vs CTRL. Data are shown as mean and SEM.</p

Experimental design employed in the present study.

<p>Subjects rested for 30 min (REST) and then performed an exercise training session (EXE) composed of approximately 10 min of a resistance eccentric exercise bout (EC) and a 90 min submaximal aerobic exercise bout (AER) (two 45 min at 70% VO₂max, interspersed by a 10 min passive rest, INT). The exercise training session was followed by four recovery strategies for 15 min (WI) in a randomized, crossover design: no immersion (CTRL), cold water immersion (CWI), temperate water immersion (TWI) and hot water immersion (HWI). After that, subjects recovered at room temperature for another 210 min (3 h30 min), divided in post-water immersion (POST-WI) and post-recovery (POST-REC). Heart rate variability indices were measured in the last 5 min of REST, WI, POST-WI and POST-REC.</p