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

Investigation on chemical composition and optimization of essential oil obtainment from waste Pinus taeda L. using hydrodistillation

ABSTRACT: The extraction of essential oils obtained by the hydrodistillation of needles/twigs waste of Pinus taeda L. was optimized by applying response surface methodology (RSM), with 24 full factorial design, in order to improve oil essential production, and aggregate value to the production chain of pine wood. Through the model it was possible to ascertain the influence of the variables in the average amount of essential oil (0.1032 mL), being the variables analyzed: biomass - Bm (x1), extraction time - ET (x2), Bm:ET (x1x2) and sample size - SS: drying times - DT (x3x4). Only linear terms (biomass and extraction time) and your interaction demonstrated significant positive values (0.0344, 0.0206 and 0.0131). The major components of the essential oil identified by GC-MS were: β-phellandrene: (30.39 and 22.44%), tricyclene (26.14 and 20.46%), β-myrcene (14.32 and 11.50%), β-pinene (22.49 and 1.43%) and α-pinene (0.25 and 11.26%) in the years 2011 and 2012, respectively. Our results show that the essential oil obtained from P. taeda represents a way of using some of the waste generated by the timber industry. The process of obtaining doesn't require treatments such as controlled drying or size reduction of the sample, indicating that it can be used in an industrial scale