Enraizamento de corticeira-da-serra em função do tipo de estaca e variações sazonais

Erythrina falcata Benth. may be used as an ornamental plant, in rehabilitation of degraded land and as a component in agroforestry systems. However seedling production from seeds is difficult. The aim of this work was to evaluate vegetative propagation of E. falcata by using stem cuttings obtained from adult trees (softwood cuttings, hardwood cuttings and regrowth cuttings) and cuttings from seedlings collected in the four seasons of the year as well as the effect of indolebutyric acid on rooting of stem cuttings. After cutting preparation, the material was treated with an indolebutyric acid solution (IBA, 0, 1.5 and 3 g L-1). Cuttings were grown in 55-mL tapered plastic containers in a greenhouse at 25 to 30°C and relative humidity above 80%. The substrate for growing of cuttings was middle texture vermiculite. The highest percentage of rooted cuttings (73%) and root length of four longest roots (46 mm) and root number (6.2) were obtained in seedling cuttings collected in the summer. No rooting was observed in cuttings collected from softwood cuttings raised from adult trees. Cutting immersion in IBA solutions had no effect on rooting. Cuttings from seedlings collected in the summer are recommended because of their high percentage of rooting and survival

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