Lightness Dependencies and the Effect of Texture on Suprathreshold Lightness Tolerances

A psychophysical experiment was performed to determine the effects of lightness dependency on suprathreshold lightness tolerances. Using a pass/fail method of constant stimuli, lightness tolerance thresholds were measured using achromatic stimuli centered at CIELAB L* = 10, 20, 40, 60, 80, and 90 using 44 observers. In addition to measuring tolerance thresholds for uniform samples, lightness tolerances were measured using stimuli with a simulated texture of thread wound on a card. A texture intermediate between the wound thread and the uniform stimuli was also used. A computer-controlled CRT was used to perform the experiments. Lightness tolerances were found to increase with increasing lightness of the test stimuli. For the uniform stimuli this effect was only evident at the higher lightnesses. For the textured stimuli, this trend was more evident throughout the whole lightness range. Texture had an effect of increasing the tolerance thresholds by a factor of almost 2 as compared to the uniform stimuli. The intermediate texture had tolerance thresholds that were between those of the uniform and full-textured stimuli. Transforming the results into a plot of threshold vs. intensity produced results that were more uniform across the three conditions. This may indicate that CIELAB is not the best space in which to model these effects

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

The Use of Color in Multidimensional Graphical Information Display

The performance in judging values in a univariate map encoded using five different color scales was tested in eleven subjects. Digital elevation maps (DEMs) were encoded using: 1) an RGB gray scale (RGB), 2) a gray scale based on CIELAB L * (L*), 3) a L * scale with an added red hue component (Red L*), 4) an L * scale with continuous hue change (Spectral L*), and 5) a gray scale based on luminance (Luminance). Performance was tested using an Evaluation task and a Production task. For both tasks judgments were made both with and without legends for all five encoding schemes. The results show a significant effect of choice of encoding scheme, the presence or absence of a legend, and an interaction between these two factors. Performance with a legend was significantly better than without one. The Spectral L * scale led to the best performance while Luminance encoding was the worst. This experiment is a first step in using quantifiable psychophysical procedures to evaluate the effectiveness of different color encoding schemes on the interpretability of multidimensional graphical images

Image quality scaling of electrophotographic prints

Two psychophysical experiments were performed scaling overall image quality of black-and-white electrophotographic (EP) images. Six different printers were used to generate the images. There were six different scenes included in the experiment, representing photographs, business graphics, and test-targets. The two experiments were split into a pairedcomparison experiment examining overall image quality, and a triad experiment judging overall similarity and dissimilarity of the printed images. The paired-comparison experiment was analyzed using Thurstone’s Law, to generate an interval scale of quality, and with dual scaling, to determine the independent dimensions used for categorical scaling. The triad experiment was analyzed using multidimensional scaling to generate a psychological stimulus space. The psychophysical results indicated that the image quality was judged mainly along one dimension and that the relationships among the images can be described with a single dimension in most cases. Regression of various physical measurements of the images to the paired comparison results showed that a small number of physical attributes of the images could be correlated with the psychophysical scale of image quality. However, global image difference metrics did not correlate well with image quality