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

Avaliação das proporções dos cortes da carcaça, características da carne e avaliação dos componentes do peso vivo de cordeiros Evaluation of carcass cuttings proportion, meat characteristics and evaluation of live weight components of lambs

Objetivou-se, neste trabalho, avaliar a composição física da carcaça, as características da carne e a proporção dos não-componentes da carcaça do peso vivo de cordeiros abatidos aos 28 kg submetidos a diferentes sistemas alimentares. Dezoito cordeiros (Ile de France x Texel ) foram distribuídos aleatoriamente, com as respectivas mães, em três tratamentos: PCA - pastagem cultivada de azevém (Lolium multiflorum Lam.), CON - confinamento sem alimentação privativa e CCF - confinamento com alimentação privativa para os cordeiros. A alimentação privativa foi oferecida aos cordeiros pelo sistema de creep feeding. Não houve diferença para o percentual do pescoço entre PCA, CON e CCF, com valores de 9,27; 9,17 e 8,72%, respectivamente. Para o percentual de perna, os animais da CON (34,02%) e CCF (34,17%) apresentaram valores semelhantes entre si e superiores aos da PCA (31,73%). A maciez medida na porção do Longissimus dorsi entre a 9ª e 12ª costelas foi semelhante entre os animais dos três tratamentos, de 2,33; 3,03 e 3,08 para PCA, CON e CCF, respectivamente. Não houve efeito dos tratamentos sobre a palatabilidade e suculência da carne. O percentual de pele dos animais da PCA (11,05%) e do CON (10,50%) foram semelhantes entre si, enquanto o PCA apresentou valores mais elevados que o percentual dos animais do CCF (9,70%). Para o percentual do conteúdo gástrico, os valores observados para os animais do CCF (11,47%) foram superiores àqueles da PCA (5,09%) e semelhantes aos obtidos para os animais do CON (8,72%).<br>This study aimed to evaluate carcass physical comp0sition, meat characteristics and live weight carcass no component of lambs slaughter at 28 kg under different feeding systems. Eighteen lambs (Ile de France X Texel) were randomly allotted, with their respective mothers, to three treatments: RP - ryegrass pasture (Lolium multiflorum Lam.), COM - confinament of lamb and sheep together and CCF - confinament with creep feeding. A private feeding by creep feeding system was offered for the lambs. There were no difference in the neck percentage among RP, COM and CCf, with values of 9.27, 9.17, and 8.72%, respectively. In relation to the legs, COM (34.02%) and CCF (34.17%) animals expressed similar values and were superior than RP(31.73) animals values. The softness measured in the loin portion (Longissimus dorsi), from 9th to 12th rib was the same in the animals submitted to three treatments: RP, COM and CCF, with respective values of 2.33, 3.03 and 3.08. There were not effects in the treatments according to juiceness (SUC) and taste (TASTE). The skin percentage of RP (11.05%) and COM (10.50%) were no treatment effects to the through the panel, palatability and juiceness. The skin percentages of RP (11.05%) and CON (10.50%) animals were similar, and RP showed higher values than CCF (9.70%). The values of gastric content percentage for CCF (11.47%) animals were higher than the RP animals (5.09%) and showed similar value for CON (8.72%)