PRODUCTION OF AMBARELLA SEEDLINGS TREATED WITH INDOLE BUTYRIC ACID AND IRRIGATED WITH REUSED WATER

Species of the genus Spondias, such as ambarella, are propagated primarily by vegetative parts. However, for planting it is necessary to use plant regulators. In addition to propagation related aspects, the availability of good quality water for seedling production in semi-arid regions is a limiting factor. The objective of this work was to evaluate the rooting of semi-herbaceous cuttings of ambarella using different concentrations of IBA conveyed in solution and talc, irrigated with different concentrations of treated domestic effluent. The experiment was carried out in a nursery with 50% shade in a completely randomized design in a 4 x 4 factorial scheme consisting of four doses of treated domestic effluent (EDT) diluted in water supply [E1 = 0% of EDT (100% AA - control); E2 = 33.3% of EDT (66.7% of AA); E3 = 66.7% of EDT (33.3% of AA) and E4 = 100% of EDT (0% AA)] used in daily irrigation and four IBA dilution managements (M1 = control without IBA, M2 = IBA carried in water at 6,000 mg L-1, M3 = IBA delivered in alcohol (70%) at 4,000 mg L-1 and M4 = IBA in Talc at 5000 mg L-1), with four replicates. The treated domestic effluent is viable for irrigation of ambarella seedlings in its diluted or concentrated. The use of 5,000 mg L-1 of IBA delivered to talc is management satisfactory for the propagation of seedlings of ambarella by means of semi-herbaceous cuttings

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