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

Gallium-Containing Hydroxyapatite as a Promising Material for Photocatalytic Performance

The development of photocatalystsor their modification to obtain new photocatalytic performances for the removal of contaminants is a challenge. Hydroxyapatite (HAp), (Ca10(PO4)6(OH)2), is an inorganic component with a high superficial area and low toxicity and the presence of metal in its structure can be an interesting strategy for the photocatalytic approach. This work aimed to synthesize gallium-containing HAp (Ga-HAp) as a promising material for photocatalytic performance. The synthesis was performed by the suspension–precipitation method. The material was characterized by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Morphological analysis employed field emission scanning electron microscope (FESEM) and the elemental analysis by energy-dispersive spectroscopy (EDS). To evaluate the photocatalytic activity, methylene blue (MB) dye was used as a pollutant model under UV light for 120 min. The influence of Ga-HAp concentration (0.25, 0.50, and 1.00 g·L−1) and kinetic reaction was also studied. The Ga-HAp was successfully obtained by the suspension–precipitation method. The structural characterization by XRD and FESEM-EDS elucidated the presence of gallium in the structure of hydroxyapatite. The XPS results indicated the substitution of gallium in the crystal lattice of the material. The discoloration rate of MB dye using Ga-Hap was calculated by pseudo first-order kinetics, and the best rate constant was 7.5 × 10−3 min−1 using 1.00 g·L−1 of photocatalyst. The concentration of Ga-HAp influenced the photocatalytic process, because the discoloration rate increased as a function of the concentration of material. Therefore, Ga-HAp is a promising material for environmental remediation

Gallium-Containing Hydroxyapatite as a Promising Material for Photocatalytic Performance

The development of photocatalystsor their modification to obtain new photocatalytic performances for the removal of contaminants is a challenge. Hydroxyapatite (HAp), (Ca10(PO4)6(OH)2), is an inorganic component with a high superficial area and low toxicity and the presence of metal in its structure can be an interesting strategy for the photocatalytic approach. This work aimed to synthesize gallium-containing HAp (Ga-HAp) as a promising material for photocatalytic performance. The synthesis was performed by the suspension–precipitation method. The material was characterized by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Morphological analysis employed field emission scanning electron microscope (FESEM) and the elemental analysis by energy-dispersive spectroscopy (EDS). To evaluate the photocatalytic activity, methylene blue (MB) dye was used as a pollutant model under UV light for 120 min. The influence of Ga-HAp concentration (0.25, 0.50, and 1.00 g·L−1) and kinetic reaction was also studied. The Ga-HAp was successfully obtained by the suspension–precipitation method. The structural characterization by XRD and FESEM-EDS elucidated the presence of gallium in the structure of hydroxyapatite. The XPS results indicated the substitution of gallium in the crystal lattice of the material. The discoloration rate of MB dye using Ga-Hap was calculated by pseudo first-order kinetics, and the best rate constant was 7.5 × 10−3 min−1 using 1.00 g·L−1 of photocatalyst. The concentration of Ga-HAp influenced the photocatalytic process, because the discoloration rate increased as a function of the concentration of material. Therefore, Ga-HAp is a promising material for environmental remediation