Primary metabolism components of seeds from Brazilian Amazon tree species

The contents of the main components of the primary metabolism (soluble sugars, starch, proteins, oils, fatty acids) and minerals (P, Ca, Mg, K, Fe, Zn, Mn, Cu) were characterized in seeds of five Brazilian Amazon tree species (Andira parviflora, Bertholletia excelsa, Helicostylis tomentosa, Hymenaea parviflora, and Parkia pendula). Soluble sugar contents were high in P. pendula seeds (14%), whereas starch predominated in A. parviflora seeds (58.7%). A. parviflora and H. parviflora seeds were rich in proteins (35.1% and 32.4%, respectively). The oil contents ranged from 1.4% in A. parviflora to 70.7% in B. excelsa. Only B. excelsa and P. pendula seeds may be considered oilseeds, with 70.7% and 28.4% oil, respectively. The fatty acid compositions showed high proportions of unsaturated fatty acids, mainly oleic and linoleic acids, regardless of the species. B. excelsa and P. pendula also showed high amounts of P, Mg, K and Zn

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

Silicon improves rice grain yield and photosynthesis specifically when supplied during the reproductive growth stage

Silicon (Si) has been recognized as a beneficial element to improve rice (Oryza sativa L.) grain yield. Despite some evidence suggesting that this positive effect is observed when Si is supplied along the reproductive growth stage (from panicle initiation to heading), it remains unclear whether its supplementation during distinct growth phases can differentially impact physiological aspects of rice and its yield and the underlying mechanisms. Here, we investigated the effects of additions/removals of Si at different growth stages and their impacts on rice yield components, photosynthetic performance, and expression of genes (Lsi1, Lsi2 and Lsi6) involved in Si distribution within rice shoots. Positive effects of Si on rice production and photosynthesis were manifested when it was specifically supplied during the reproductive growth stage, as demonstrated by: (1) a high crop yield associated with higher grain number and higher 1000-grain weight, whereas the leaf area and whole-plant biomass remained unchanged; (2) an increased sink strength which, in turn, exerted a feed-forward effect on photosynthesis that was coupled with increases in both stomatal conductance and biochemical capacity to fix CO2; (3) higher Si amounts in the developing panicles (and grain husks) in good agreement with a remarkable up-regulation of Lsi6 (and to a lesser extent Lsi1). We suggest that proper levels of Si in these reproductive structures seem to play an as yet unidentified role culminating with higher grain number and size