O ácido giberélico promove a superação da dormência de sementes de arroz e a formação de plântulas anormais

O arroz é uma das espécies que apresenta dormência após a colheita, podendo esta ser prolongada durante o armazenamento das sementes. Este trabalho teve por objetivo determinar se o ácido giberélico (GA3) é um promotor eficiente da superação de dormência em sementes de arroz e avaliar mudanças nas estruturas biológicas via histoquímica. A cultivar utilizada foi a SCS122 Miura submetida a 0 mg L-1, 500 mg L-1 e 1000 mg L-1 de GA3. Foram realizadas análises de germinação, viabilidade, comprimento de raiz, parte aérea e plântula, microscopia óptica do amido e quantificação dos açúcares solúveis totais. A utilização de 500 mg L-1 e 1000 mg L-1 de GA3 foi eficiente para a superação da dormência de sementes de arroz, reduzindo o percentual de sementes dormentes para 4% e 1% respectivamente. Apesar de reduzir o percentual dormência, a presença de GA3 provoca aumento do percentual de plântulas anormais, e por isso, nas concentrações utilizadas, não pode ser recomendado como método de superação em sementes de arroz. A microscopia óptica é eficiente para verificar que com a superação de dormência, ocorre a degradação dos grânulos de amido, aumentando a disponibilidade de açúcares solúveis totais para o crescimento e desenvolvimento de plântulas.Rice is one species that present dormancy after harvest and can be prolonged during seed storage. This work aimed to determine whether gibberellic acid (GA3) is an efficient promoter of dormancy-breaking in rice seeds and evaluate changes in biological structures via histochemistry. The cultivar used was SCS122 Miura submitted to 0 mg L-1, 500 mg L-1, and 1000 mg L-1 of GA3. Germination, viability, root, shoot and seedling length, starch optical microscopy, and quantification of total soluble sugars were performed. The use of 500 mg L-1 and 1000 mg L-1 of GA3 was efficient in dormancy-breaking rice seeds, reducing the percentage of dormant seeds to 4% and 1%, respectively. Despite lowering the dormancy percentage, the presence of GA3 causes an increase in the percentage of abnormal seedlings. Therefore, it cannot be recommended as a method of dormancy-breaking rice seeds at the concentrations used. Optical microscopy is efficient to verify that with the dormancy-breaking, the degradation of starch granules occurs, increasing the availability of total soluble sugars for the growth and development of seedlings

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