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

Production parameters and forage loss of oat and rye grass pastures managed with beef heifers fed diets with energy supplementation

Production parameters of intercropped pastures of oat and rye grass managed with beef heifers supplemented with brown rice meal and/or protected fat were evaluated. Twenty-eight Charolais × Nellore crossbred heifers at initial average age of 18 months and initial average live weight of 274.9 kg were utilized in the experiment. Animals were kept on oat + rye grass pastures and distributed in the following treatments: no-supplementation (NS): heifers kept only in pastures; Megalac (MEG): supplementation with protected fat; brown rice meal (BRM): supplementation with BRM; BRM + MEG: supplementation with BRM plus protected fat. The greater participation of oat leaf was from July 5th to August 10th, 2009 and of rye grass, from August 30th to September 26th, 2009. The crude protein content increased until the 55th day (225.1 g/kg). Pasture total digestible nutrients presented a cubic behavior, with an average of 722.0 g/kg. The highest supply of leaf blades, 5.17 kg of dry matter/100 kg of live weight, was found in the second period. Pasture intake increased throughout the periods. Forage mass and support capacity of the animal did not differ between treatments, presenting means of 1245.02 kg of dry matter/ha and 882 kg of live weight/ha, respectively. Stoking rate, forage loss and pasture intake were not affected by the treatments. Supplementation of beef heifers with rice meal and/or protected fat did not change production parameters of oat + rye grass pastures or pasture intake. Increase in daily accumulation rate of dry matter and supporting capacity of the animals increases forage losses