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

Development and reproductive performance of Hereford heifers of different frame sizes up to mating at 14-15 months of age

ABSTRACT Body development and reproductive performance of a hundred forty-two 14 to 15-month-old heifers, classified at weaning according to frame size as small, medium, and large, were evaluated. The parameters evaluated were: body weight, hip height, body condition score, weight gain, ovarian activity, and pregnancy rate. At weaning, body weight and hip height were significantly different among frame scores, (small – 133.0 kg, 92.2 cm; medium – 158.5 kg, 96.6 cm; and large – 185.2 kg; 100.2 cm). After weaning, heifers grazed together on natural pastures during the autumn and on ryegrass (Lolium multiflorum La.) during the winter and spring. Frame score differences remained until the beginning of the breeding season (BS), starting on average at 14 months of age. Weight gain between weaning and the beginning of BS was not different among frame scores (0.740 kg/day, on average). Body weights at the beginning of the BS were significantly different, of 255.7 kg (53.3% of the mature weight) for small heifers, 285.0 kg (59.4%) for medium heifers, and 307.6 kg (64.1%) for large heifers. Ovarian activity at the beginning of the BS was not different among the three groups. The average weight gain values during the BS of 0.492, 0.472, and 0.421 kg/day for small, medium, and large heifers, respectively, were significantly different. Pregnancy rates were not different among groups (small, 71.4%; medium, 76.4%; and large, 76.5%). Frame score did not influence the reproductive performance of heifers, but the small and medium heifers conceived 29 and 20 days earlier, respectively, than the large heifers

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

Interaction between ractopamine and growth hormone in the metabolism of hypophysectomized female rats

Ractopamine and growth hormone have been extensively studied due to their ability to generate a better partition of nutrients in the body, providing an increased muscle protein synthesis and lipolysis in adipose tissue. Thus, this article aims to check the effects of interaction between these substances on the metabolism of hypophysectomized female rats, and their individual effects on the body composition of these animals. Thirty Fisher rats were distributed into five treatments, one of them was a normal control group, one was a hypophysectomized control group, and the other three were hypophysectomized animal groups treated with ractopamine (80 mg/kg/day), with growth hormone (4 mg/kg/day), and with a combination of them, all with six replicates in each group. The association between these substances provided a higher percentage of protein and decreased ether extract in the animals’ carcass. Furthermore, it caused an increase in water intake, in urine production, and decreased relative weight of kidneys, liver, and spleen when compared to the control group. The use of growth hormone provided a higher final weight gain and feeding effectiveness, lower heart weight and increased blood glucose level, and the use of ractopamine resulted in a higher lung weight, increased total cholesterol and IGF-1, and decreased peptide C concentration

Interação ractopamina e hormônio do crescimento no metabolismo de ratas hipofisectomizadas

http://dx.doi.org/10.5007/2175-7925.2013v26n4p241A ractopamina e o hormônio do crescimento têm sido muito estudados por conta de sua capacidade de gerar uma melhor partição de nutrientes no organismo, proporcionando aumento na síntese proteica muscular e lipólise no tecido adiposo. Assim, este artigo tem por objetivo verificar os efeitos da interação entre essas substâncias no metabolismo de ratas hipofisectomizadas e seus efeitos individuais sobre a composição corporal desses animais. Trinta ratas Fisher foram distribuídas em cinco tratamentos, sendo um grupo controle normal, um controle hipofisectomizado e os outros três grupos de animais hipofisectomizados tratados com ractopamina (80 mg/kg/dia), com hormônio do crescimento (4 mg/kg/dia) e com a associação dos dois, todos com seis repetições em cada grupo. A associação das duas substâncias proporcionou maior porcentagem de proteína e diminuição do extrato etéreo na carcaça dos animais. Além disso, ocasionou aumento na ingestão de água, na produção urinária e diminuição do peso relativo dos rins, fígado e baço quando comparado ao grupo controle. O uso do hormônio do crescimento proporcionou maior ganho de peso final e eficiência alimentar, menor peso cardíaco e aumento da glicemia, e o uso da ractopamina ocasionou maior peso pulmonar, aumento no colesterol total e IGF-1 e diminuição na concentração de peptídeo C

Schematic representation of the experimental design across time.

<p>¹All animals were acclimated in individual metabolic cages for seven days; ²For animals in the following groups: diabetes; diabetes + periodontal disease; diabetes + β-glucan; diabetes + periodontal disease + β-glucan; ³For animals in the following groups: β-glucan; diabetes + β-glucan; periodontal disease + β-glucan; diabetes + periodontal disease + β-glucan; ⁴For animals in the following groups: periodontal disease; diabetes + periodontal disease; periodontal disease + β-glucan; diabetes + periodontal disease + β-glucan; ⁵Collection of blood samples and removal of the jaws.</p

Alveolar bone loss in animals treated with β-glucans (30 mg/kg/day for 28 days).

<p>A—control; B—diabetes; C—periodontal disease; D—β-glucan; E—diabetes + periodontal disease; F—diabetes + β-glucan; G—periodontal disease + β-glucan, H—diabetes + periodontal disease + β-glucan. Only one representative animal per group is depicted in this figure.</p