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

Properties of extruded xanthan-starch-clay nanocomposite films

The aim of this work was to manufacture the biodegradable nanocomposite films by extrusion from different combinations of cassava starch, xanthan gum and nanoclays (sodium montmorillonite - MMT- Na) and to characterize them according to their microstructure, optical, mechanical and barrier properties. Films were manufactured from nine starch/xanthan/nanoclay combinations, containing glycerol as plasticizer. Scanning electron microscopy (SEM) of the starch-xanthan extruded films showed reticulated surface and smooth interior, indicating that the gum was mostly concentrated on the surface of the films, while starch/xanthan/nanoclays films showed a more homogeneous surface, suggesting that the introduction of nanoclays provided a better biopolymeric interaction. In general, nanoclays addition (2.5 - 5.0, w%) generated more transparent and resistant films, with lower water vapor permeabilities and lower water sorption capacities and xanthan gum addition improved the elongation ofa starch films

Citric acid as multifunctional agent in blowing films of starch/PBAT

Citric acid was used as a compatibilizer in the production of starch and PBAT films plasticized with glycerol and processed by blow extrusion. Films produced were characterized by WVP, mechanical properties, FT-IR-ATR and SEM. WPV ranged from 3.71 to 12.73×10-11 g m-1 s-1 Pa-1, while tensile strength and elongation at break ranged from 1.81 to 7.15 MPa and from 8.61 to 23.63%, respectively. Increasing the citric acid concentration improved WVP and slightly decreased film resistance and elongation. The films micrographs revealed a more homogeneous material with the addition of citric acid. However, the infrared spectra revealed little about cross-linking esterification reactio

Sericin as compatibilizer in starch/ polyester blown films

Abstract This study investigated the effects of low concentrations of sericin (≤ 1.5 wt%) in starch- poly(butylene-adipate-co-terephthalate) (PBAT) films. The films were produced by blown extrusion and mechanical, barrier and structural properties were determined. Films containing 1.0 and 1.5 wt% sericin showed higher tensile strength (6.41 and 6.59 MPa) and Young's modulus (90.88 and 132.71 MPa) compared with film without sericin (4.76 MPa and 18.64 MPa). When 0.5 wt% of sericin was used, the elongation was reduced by 62%. The addition of sericin in a concentration of 1.5% (w/w) decreased the water vapor permeability of films from 7.55 to 5.94 g (m s Pa)-1, likely due to the formation of a more homogeneous and compact matrix. Based on these results, a mechanism of action is proposed, whereby sericin acts at the interface of the polymers (starch and PBAT), reducing the interfacial tension and enhancing compatibility