In vitro germination and growth of babassu (orbygnia phalerata Mart.) embryos subjected to different drying temperatures

The aim of this study was to evaluate the physiological quality of babassu (Orbygnia phalerata Mart.) embryos in relation to drying rate. The fruits were kept in a dry chamber with forced air circulation at 57 ± 2°C and 37 ± 2°C for 0, 6 and 11 days. An interaction between the drying time and temperature on water loss was not observed. Fruits dried at 37 ± 2°C failed to achieve the same water content values as those dried at 57 ± 2°C. Embryos dehydrated at 37 ± 2°C remained viable even after 11 days of drying, while embryos dehydrated at 57 ± 2°C were dead after 6 days of drying. Germination percentages above 67% were obtained for all times at drying 37°C, even for seeds with 9% water content, which highlights a possible orthodox behavior. Under the experimental conditions of the present study, drying embryos at 57 ± 2°C decreased the percentage and speed of germination as well as the initial growth of seedlings.Keywords: Arecaceae, desiccation tolerance, orthodox seed

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

Effects of drying temperature on viability of macaw palm (Acrocomia aculeata) zygotic embryos

Submitted by Luciana Ferreira ([email protected]) on 2018-04-12T14:36:10Z No. of bitstreams: 2 Artigo - Aurélio Rubio Neto - 2015.pdf: 755268 bytes, checksum: 3ebba181e8c41aebbbc7cc934c250568 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira ([email protected]) on 2018-04-13T11:55:45Z (GMT) No. of bitstreams: 2 Artigo - Aurélio Rubio Neto - 2015.pdf: 755268 bytes, checksum: 3ebba181e8c41aebbbc7cc934c250568 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2018-04-13T11:55:45Z (GMT). No. of bitstreams: 2 Artigo - Aurélio Rubio Neto - 2015.pdf: 755268 bytes, checksum: 3ebba181e8c41aebbbc7cc934c250568 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2015-01In response to a growing interest in improving seedling production of oilseed species (like macaw palm), a fruit drying protocol for facilitating seed extraction was proposed. This enabled the production of macaw palm seedlings, but the temperature most suitable for seed extraction without losing its physiological quality is unknown. The goal of this study was to evaluate the effects of different drying temperatures on the physiological quality of macaw palm zygotic embryos to improve previously published drying methods. Fruits were dried in a forced-air drying oven at 57 or 37°C at different time periods (zero, two, four, six and eight days). Following each drying period, the fruits were removed from the drying oven, and the water content of the fruits and seeds were measured in addition to embryo viability and in vitro germination. Seed water content could be estimated based on fruit water content at both drying temperatures, eliminating the need to remove the seeds from the fruit. Drying at 57°C decreased the drying time by 50% compared to drying at 37°C; however, it was detrimental to embryo viability and germination. Therefore, drying of fruit at 37°C is recommended. Embryos dried at this temperature were still able to germinate after 16-day drying period, which corresponded to a decrease of 24.8% in the initial fruit water content