GERMINATION EFFECT OF DIFFERENT SUBSTRATES ON GRUMIXAMEIRA

Grumixama belongs to the Myrtaceae family, members of which have simple leaves, which are generally leaf-opposed, with whole margins, an evergreen color. The Myrtaceae family includes about 140 genera and over 3,000 species. Grumixama is rarely cultivated and its production is normally used in the manufacture of bulk candy, syrup, liqueurs and jams. Because it is rarely cultivated there is virtually no agronomic information about the cultivation of this fruit tree, in particular relating to seedling production and seed germination. The objective of this study was to evaluate grumixama seed germination in two substrates; Tecnomax® (based on pine bark) and washed sand. Sowing was performed in 110 cm3 volume, high-density polyethylene tubes filled with the respective substrates. Sixty days after sowing the germination percentage was evaluated. The height, stem diameter and number of leaves were evaluated at 120 days, when the seedlings had an average height of 10 cm. The pine bark substrate provided the best germination and development of quality grumixama seedlings compared to the washed sand

Feijão guandu cru na alimentação de frangos caipiras criados em sistema semi-intensivo

O objetivo deste trabalho foi avaliar os efeitos da substituição do farelo de soja pelo feijão guandu cru na alimentação de frangos caipiras criados em sistema semi-intensivo. Foram utilizados 525 frangos de corte da linhagem Caipira Pesadão, com idade inicial de 35 dias, distribuídos em cinco tratamentos com cinco repetições de 21 aves cada um. Os tratamentos consistiram na substituição de 0, 5, 10, 15 e 20% do farelo de soja pelo feijão guandu cru moído. Foram avaliados o ganho de peso, o consumo de ração, a conversão alimentar, o rendimento de carcaça e de cortes, o peso do pâncreas e a qualidade da carne. A substituição do farelo de soja pelo feijão guandu em até 15,45%, nas dietas de frangos caipiras de corte, com idade de 57 a 71 dias, não altera o ganho de peso. O aumento dos níveis de feijão guandu na ração não afeta o rendimento de carcaça, o peso do pâncreas e os parâmetros de qualidade da carne

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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

Método para determinação de volume específico como padrão de qualidade do polvilho azedo e sucedâneos

Objetivou-se estabelecer um método para a determinação da expansão como critério de qualidade do polvilho azedo e sucedâneos. Três tipos de amidos de mandioca foram avaliados: Polvilho Azedo (padrão), Expandex® e Amido Nativo. As amostras foram condicionadas diretamente em moldes de silicone e metal revestido com politetrafluoretileno e forneadas sob condições controladas de temperatura 200 ºC ou 250 °C e tempo 20 ou 25 minutos. O método proposto foi avaliado pela repetitividade do volume específico (relação volume:massa) e os resultados foram comparados com os obtidos por método tradicional com manuseio dos ingredientes com água fervente e moldeamento manual em bolas, posteriormente forneadas em assadeira de alumínio untada, assadas nas mesmas condições. A seleção do melhor método levou em conta os maiores valores para volume específico (cm3 g–1) e menores variações entre as repetições. Para a fôrma de silicone, os melhores resultados foram obtidos nas condições de 200 °C e 25 min (8,89 cm3 g–1 com 1,46% de variabilidade), que proporcionou massas suficientemente resistentes ao manuseio para permitir as medidas. Para moldes de politetrafluoroetileno as melhores condições foram temperatura de 200 °C durante o tempo de 20 minutos (8,16 cm3 g–1 com 7,59% de variabilidade). A amostra de Expandex® apresentou a mesma resposta que o polvilho azedo às variáveis de temperatura de forno e tempo de assamento, porém com valores maiores. A presença residual de grânulos intactos mostrou que a gelatinização do amido não foi completa nas condições de forneamento. O método proposto apresentou facilidade na manipulação e o tempo necessário foi apenas aquele necessário para a elaboração da pasta com água e o forneamento