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

Miuda (Nopalea cochenillifera (L.) Salm-Dyck)-The Best Forage Cactus Genotype for Feeding Lactating Dairy Cows in Semiarid Regions

Simple Summary The usage of forage cactus is essential for the maintenance of livestock activity in semiarid regions as an alternative to conventional crops. Cactaceae have adaptive characteristics that ensure their development progress under drought conditions. Four genotypes of forage cactus (Gigante, Miuda, IPA Sertania, and Orelha de Elefante Mexicana) were fed to lactating dairy cows and the diets were then evaluated based on animal performance, milk fatty acid profile, and microbial protein synthesis. Miuda forage cactus led to a higher nutrient intake and milk yield, as well as greater microbial protein synthesis. Higher saturated fatty acids were observed when the Gigante and IPA Sertania forage cactus genotypes were fed to dairy cows. Orelha de Elefante Mexicana forage cactus caused lower milk yield along with protein yields and content; however, it improved the milk fatty acid profile by promoting a higher ratio of unsaturated to saturated fatty acids and desirable fatty acids. It is concluded that the Miuda forage cactus is the genotype most suitable for the diets of lactating dairy cows. This study aimed to investigate the effects on nutrient intake and digestibility, milk yield (MY) and composition, milk fatty acids profile, and microbial protein synthesis caused by feeding lactating dairy cows four different forage cactus genotypes. Eight Girolando cows (5/8 Holstein x 3/8 Gyr), weighing 490 +/- 69.0 kg (means +/- standard deviation), and producing 15.5 +/- 1.0 kg/d of milk during pretrial were distributed to two contemporaneous 4 x 4 Latin squares. The cows were fed a total mixed ration composed of sorghum silage (385 g/kg of dry matter (DM)), concentrated mix (175 g/kg DM), and forage cactus (440 g/kg DM). The experimental treatments consisted of different cactus genotypes, such as Gigante cactus (GC), Miuda cactus (MC), IPA Sertania cactus (SC), and Orelha de Elefante Mexicana cactus (OEMC). The feeding of MC provided a higher intake of DM, organic matter (OM), and total digestible nutrients, as well as higher MY, energy-corrected milk, and microbial protein synthesis in comparison with those resulting from the other genotypes tested. The GC promoted lower DM and OM, and the apparent digestibility of neutral detergent fiber. The cows fed with OEMC showed lower MY and milk protein yield and content, and higher unsaturated over saturated fatty acid ratio in milk. Miuda forage cactus increased nutrient intake, digestibility of DM and OM, and microbial synthesis without impairing the milk fatty acid profile