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

Use of response surface methodology for optimization of xylitol production by the new yeast strain Debaryomyces hansenii UFV-170

Aim of this work was the optimization of xylitol production by Debaryomyces hansenii UFV-170, which already proved to be a new promising xylitol-producing yeast. Two sets of batch bioconversion tests were carried out on synthetic medium, according to two joined 3^3 and 3^2 type full factorial designs, selecting the initial xylose concentration (S0), rotational speed (v) and starting biomass concentration (X0) as independent variables and the maximum xylitol concentration (P), xylitol yield on consumed xylose (YP/S), volumetric productivity (QP) and specific productivity (qP) as response variables. The collected results were then worked out by response surface methodology (RSM). Overall optimization, conducted by overlaying the curves of the responses under investigation, allowed us to point out an optimal range of the independent variables within which the four responses were simultaneously optimized. The point chosen as representative of this optimal area corresponded to S0 = 156 g L^−1, v = 280 rpm and X0 = 6.4 g L^−1, conditions under which the model predicted P = 116.25 g L^−1, YP/S = 0.77 g g^−1, QP = 1.49 g L^−1 h^−1 and qP = 0.16 g g^−1 h^−1

Production and Characterization of β-Glucanase Secreted by the Yeast Kluyveromyces marxianus

An extracellular β-glucanase secreted by Kluyveromyces marxianus was identified for the first time. The optimal conditions for the production of this enzyme were evaluated by response surface methodology. The optimal conditions to produce β-glucanase were a glucose concentration of 4 % (w/v), a pH of 5.5, and an incubation temperature of 35 °C. Response surface methodology was also used to determine the pH and temperature required for the optimal enzymatic activity. The highest enzyme activity was obtained at a pH of 5.5 and a temperature of 55 °C. Furthermore, the enzyme was partially purified and sequenced, and its specificity for different substrates was evaluated. The results suggest that the enzyme is an endo-β-1,3(4)-glucanase. After optimizing the conditions for β-glucanase production, the culture supernatant was found to be effective in digesting the cell wall of the yeast Saccharomyces cerevisiae, showing the great potential of β-glucanase in the biotechnological production of soluble β-glucan

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans—anteaters, sloths, and armadillos—have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard to sloth species, Bradypus variegatus has the most records (n = 962), and Bradypus pygmaeus has the fewest (n = 12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans data set. Please cite this data paper when using its data in publications. We also request that researchers and teachers inform us of how they are using these data