Anatomical Specializations for Nocturnality in a Critically Endangered Parrot, the Kakapo (Strigops habroptilus)

The shift from a diurnal to nocturnal lifestyle in vertebrates is generally associated with either enhanced visual sensitivity or a decreased reliance on vision. Within birds, most studies have focused on differences in the visual system across all birds with respect to nocturnality-diurnality. The critically endangered Kakapo (Strigops habroptilus), a parrot endemic to New Zealand, is an example of a species that has evolved a nocturnal lifestyle in an otherwise diurnal lineage, but nothing is known about its' visual system. Here, we provide a detailed morphological analysis of the orbits, brain, eye, and retina of the Kakapo and comparisons with other birds. Morphometric analyses revealed that the Kakapo's orbits are significantly more convergent than other parrots, suggesting an increased binocular overlap in the visual field. The Kakapo exhibits an eye shape that is consistent with other nocturnal birds, including owls and nightjars, but is also within the range of the diurnal parrots. With respect to the brain, the Kakapo has a significantly smaller optic nerve and tectofugal visual pathway. Specifically, the optic tectum, nucleus rotundus and entopallium were significantly reduced in relative size compared to other parrots. There was no apparent reduction to the thalamofugal visual pathway. Finally, the retinal morphology of the Kakapo is similar to that of both diurnal and nocturnal birds, suggesting a retina that is specialised for a crepuscular niche. Overall, this suggests that the Kakapo has enhanced light sensitivity, poor visual acuity and a larger binocular field than other parrots. We conclude that the Kakapo possesses a visual system unlike that of either strictly nocturnal or diurnal birds and therefore does not adhere to the traditional view of the evolution of nocturnality in birds

Clay preparation and function of the first ceramics in north-west Anatolia: A case study from Neolithic Barcın Höyük

Abstract Background Based on evolutionary patterns of the vertebrate eye, Walls (1942) hypothesized that early placental mammals evolved primarily in nocturnal habitats. However, not only Eutheria, but all mammals show photic characteristics (i.e. dichromatic vision, rod-dominated retina) suggestive of a scotopic eye design. Results Here, we used integrative comparative genomic and phylogenetic methodologies employing the photoreceptive opsin gene family in 154 mammals to test the likelihood of a nocturnal period in the emergence of all mammals. We showed that mammals possess genomic patterns concordant with a nocturnal ancestry. The loss of the RH2, VA, PARA, PARIE and OPN4x opsins in all mammals led us to advance a probable and most-parsimonious hypothesis of a global nocturnal bottleneck that explains the loss of these genes in the emerging lineage (> > 215.5 million years ago). In addition, ancestral character reconstruction analyses provided strong evidence that ancestral mammals possessed a nocturnal lifestyle, ultra-violet-sensitive vision, low visual acuity and low orbit convergence (i.e. panoramic vision). Conclusions Overall, this study provides insight into the evolutionary history of the mammalian eye while discussing important ecological aspects of the photic paleo-environments ancestral mammals have occupied

A novel method for comparative analysis of retinal specialization traits from topographic maps

Vertebrates possess different types of retinal specializations that vary in number, size, shape, and position in the retina. This diversity in retinal configuration has been revealed through topographic maps, which show variations in neuron density across the retina. Although topographic maps of about 300 vertebrates are available, there is no method for characterizing retinal traits quantitatively. Our goal is to present a novel method to standardize information on the position of the retinal specializations and changes in retinal ganglion cell (RGC) density across the retina from published topographic maps. We measured the position of the retinal specialization using two Cartesian coordinates and the gradient in cell density by sampling ganglion cell density values along four axes (nasal, temporal, ventral, and dorsal). Using this information, along with the peak and lowest RGC densities, we conducted discriminant function analyses (DFAs) to establish if this method is sensitive to distinguish three common types of retinal specializations (fovea, area, and visual streak). The discrimination ability of the model was higher when considering terrestrial (78%-80% correct classification) and aquatic (77%-86% correct classification) species separately than together. Our method can be used in the future to test specific hypotheses on the differences in retinal morphology between retinal specializations and the association between retinal morphology and behavioral and ecological traits using comparative methods controlling for phylogenetic effects. Keywords: fovea, ganglion cells, retina, topographic maps, visual ecology, visual streak Citation: Moore, B. A., Kamilar, J. M., Collin, S. P., Bininda-Emonds, O. R. P., Dominy, N. J., Hall, M. I., et al. (2012). A novel method for comparative analysis of retinal specialization traits from topographic maps. Journal of Vision, 12(12):13, 1-24, http://www.journalofvision.org/content/12/12/13, doi:10.1167/12.12.13. Introduction The vertebrate retina is a thin layer of neural tissue lining the back of the eye that samples visual information from the environment before it reaches the visual centers of the brain. Photoreceptor cells are responsible for absorbing light energy or photons and transforming these into electrical signals that pass through a series of interneurons (bipolar, amacrine, and horizontal cells) before reaching the retinal ganglion cells (RGCs), whose axons form the optic nerve. The optic nerve is organized so that retinotopic information processed at the level of the retina is carried to specific regions of the central nervous system Across vertebrates, different types of retinal specializations have been identified, such as foveae, areae, and visual streaks, each varying in number, size, shape, and position in the retina Studying the distribution of neurons across the retina, or retinal topography, of a given species can help us understand how organisms visually perceive their environment, which ultimately affects their behavior The comparative assessment of the diversity in retinal topography has important implications for better understanding the adaptations of the vertebrate visual system to different environmental conditions. This is particularly relevant given the large number of species whose retinal topography has been examined. Collin (2008) collated published topographic maps and released a public archive (see http://www.retinalmaps. com.au/) with over 300 species of vertebrates and over 1,000 maps. Despite some studies characterizing cell density gradients across the retina The aim of this study is to present a novel method to quantify the position of the retinal specialization and the concomitant changes in cell density across the retina. Additionally, using a commonly used statistical tool (discriminant function analysis [DFA]), we determined whether traits obtained by our method (retinal specialization position and cell density gradients) in combination with other retinal traits (peak and lowest ganglion cell densities) would be sensitive enough to distinguish among three common types of retinal specializations (fovea, area, or visual streak) in terrestrial and aquatic vertebrates. The methodological procedures presented in this study will have wide applicability in a comparative context by allowing us to standardize the measurement of retinal features from already published topographic maps in species with different eye size, orbit position in the skull, and overall retinal cell density. Methods This section is divided in three main parts. First, we describe the database on topographic maps gathered for this study. Second, we explain in detail the novel method we used to collect information on retinal specialization position, ganglion cell density gradients, and peak and lowest cell densities from topographic Topographic maps database We used published topographic maps of the RGC layer instead of the photoreceptor layer because they are more readily available in the literature. The original data consisted of counts of RGCs in different regions of the retina that were used to build the topographic maps. Most of the maps used in this study are available in the retinal topographic map database: http://www. retinalmaps.com.au/ We chose topographic maps that provided the orientation and scale of the retina with easily distinguished and properly labeled iso-density lines. We classified retinal specializations into three categories (fovea, area, and visual streak) based on the descriptions and topographic maps presented in the original published papers and some specific criteria (details in Appendix 2). In a limited number of studies, more than one map per species was available, and we chose the one the authors deemed most representative. The topographic map of each species was taken as the unit upon which we made measurements on different retinal traits (see below). From the topographic maps (see example in Retinal parameters Position of the retinal specialization We first established the location of the center of the retinal specialization in the topographic map. For a fovea, given its relatively small size, the position was generally marked in the topographic map as a point. The fovea can be identified from a wholemounted retina as a circular pit on the retinal tissue. However, the area and the visual streak occupy a relatively larger spatial extent than the fovea To quantify the position of the retinal specialization, we used a Cartesian coordinate system (see also Journal of Vision We converted the angle of the retinal specialization (H) and its distance to the center of the retina (r) into Cartesian coordinates, which are both linear (x and y) and can be any positive or negative number Cell density gradient across the retina Topographic maps provide a visual representation of variations in cell density across the retina using lines (iso-density lines or contours, We set sampling points along two pairs of vectors (nasal-temporal and dorsal-ventral; First, we measured the distance (mm) between isodensity lines along a given vector (nasal-temporal and dorsal-ventral; The number of sampling points (21) along a given vector allowed us to capture the high diversity in isodensity line configurations present in the published topographic maps used in this study. We tried using fewer sampling points, but missed changes in isodensity categories in some of the topographic maps. In some cases, some of the 21 sampling points did not fall within the peak density range of the retinal specialization. To determine whether or not this caused a significant change in our slope estimates, we increased the number of sampling points to include the cell density range of the retinal specialization and recalculated the slope. We found that these two measurements were highly correlated (nasal, r ¼ 0.99, p , 0.001; temporal, r ¼ 0.96, p , 0.001; dorsal, r ¼ 0.99, p , 0.001; ventral, r ¼ 0.99, p , 0.001). Consequently, we decided to use the 21 sampling points to be consistent across all topographic maps. In some cases, the published topographic maps did not include the RGC density for the outer perimeter of the retina. For these maps, when a sampling point fell into the peripheral cell density range, we established that the cell density would be half of the density of the first iso-density line shown nearest the periphery, based At each point, we measured the mean cell density value that it fell in. (c) Example of the plot of the mean cell density in each sampling point from the temporal periphery of the retina to the center of the retinal specialization. We fitted a line and used its slope as the rate of change in cell density from the retinal periphery to the retinal specialization. Journal of Vision For instance, in some topographic maps (pigmented rabbit, black bream, painted flutemouth, spookfish, and staghorn damselfish), we could only get two different cell density values on a specific retinal direction (e.g., a plateau followed by a sudden increase in cell density) because of the low number of iso-density categories or because the retinal specialization was too close to the edge of the retina, reducing the number of sampling points on that specific direction of the retina. For the linear approach, we fitted the data with a Multivariate Adaptive Regression Splines (MARSplines) analysis, which yielded a weighted slope based on slopes from lines fitted to different parts of the relationship based on differences in the coefficient of determination Journal of Vision Downloaded from jov.arvojournals.org on 06/30/2019 these species, but the overall classification scores were very similar to the analysis including these species (available from the corresponding author upon request). We therefore included these five species in the analyses to assess the discrimination ability of the model based on a wide range of retinal topographic configurations. Peak and lowest cell density From the original publications and the topographic maps, we obtained the peak RGC density. The lowest cell density was obtained from the topographic maps as the cell density at the periphery of the retina. In some cases, the cell density at the periphery was not available. We then established the cell density as half of the density of the first iso-density line reported in the topographic map (see below). Statistical analysis The analysis included measurements from 26 foveae, 35 visual streaks, and 33 areae. Six species were represented twice in our dataset (Appendix 1) due to the presence of two retinal specializations in different regions of their retinas: Chilean eagle and American kestrel (central and temporal foveae), and rock pigeon, great kiskadee, and rusty-marginated flycatcher (central fovea and area temporalis), and harlequin tuskfish (streak and area). We decided to include the second retinal specialization from each of these species due to the different morphologies within each retina (e.g., the central retinal specialization had a higher cell density than the temporal) and to determine if our method could tell the two types of specializations apart on a given species. However, we acknowledge that this introduced a bias by having two data points from each of these six species. We justified this on the basis that this study focuses on presenting a novel method rather than analyzing retinal configurations from a comparative perspective controlling for the effects of phylogenetic relatedness. We used a DFA For the DFA, we used Wilks' Lambda as the test statistic, which was then used to estimate an F statistic and p-value. Given that some of the traits we measured had a high degree of correlation (.0.70; peak RGC density and nasal, dorsal, and ventral gradient in cell density), we used a forward stepwise selection method to enter the traits in the model. This model selection procedure enhanced the classification score of the DFA in comparison to standard selection procedures forcing all traits into the model. In the DFA, we used a-priori classification probabilities that were proportional to group sizes Results We obtained measurements on all the retinal traits from 94 topographic maps belonging to 88 species of vertebrates Considering all species, the DFA with a linear approach selected five factors out of the eight: nasal and dorsal gradients in cell density, lowest RGC density, and x-and y-coordinate positions of the retinal specialization. With these factors, the DFA significantly discriminated among the three retinal specializations, F(10, 174) ¼ 6.37, p , 0.001. This DFA correctly classified 66% of the retinal specializations to the correct type. The visual streak (28 out of 35, 80%) and the fovea (16 out of 26, 61.5%) had the highest classification scores, whereas the area (18 out of 33, 54.6%) had the lowest. The DFA with a nonlinear approach selected six factors that yielded a significant discrimination among retinal specializations, F(12, 172) ¼ 5.24, p , 0.001: nasal, dorsal, and ventral PCA factors representing the gradients in cell density, lowest RGC density, and x-and y-coordinate positions of the retinal specialization. The DFA with a nonlinear approach correctly classified 67% of the retinal specializations to the correct type. The visual streak (30 out of 35, 85.7%) had the highest classification scores, followed by the fovea (15 out of 26, 57.7%) and the area (18 out of 33, 54.6%). Models with both approaches (linear and nonlinear) performed at similar levels. We found that sorting species out into terrestrial versus aquatic increased the overall classification scores of the DFA models. Considering terrestrial species, five factors were selected by the DFA with a linear approach to discriminate significantly among the retinal specializations, F(10, 104) ¼ 11.18, p , 0.001: peak and lowest RGC densities, temporal gradient in cell density, x-and y-coordinate positions of the retinal specialization. This DFA model increased the overall classification score of the 59 topographic maps of terrestrial species to 77.97%. The visual streak (23 out of 24, 95.8%) and the fovea (20 out of 22, 90.9%) had the highest classification scores, whereas the area (3 out of 13, 23.1%), the lowest. In nine mammal species, the area was misclassified as a visual streak ( When considering only the aquatic species, seven factors were selected by the DFA with the linear approach for quantifying cell density gradients to discriminate significantly among the retinal specializations, F(14, 52) ¼ 3.06, p ¼ 0.002: x-and y-coordinate positions of the retinal specialization, peak and lowest RGC densities, and temporal, nasal, and dorsal gradients in cell density. This DFA model assigned 85.7% of the topographic maps to the correct type of retinal specialization (Appendix 2). The area had the highest classification scores (18 out of 20, 9%), whereas the visual streak (9 out of 11, 81.8%) and the fovea (3 out of 4, 75%) had the lowest classification scores. In this DFA model, the most common misclassifications were visual streaks that were sorted as areae in two fish species The plot of the first and second canonical axis scores (roots 1 and 2 i

A novel method for comparative analysis of retinal specialization traits from topographic maps

Vertebrates possess different types of retinal specializations that vary in number, size, shape, and position in the retina. This diversity in retinal configuration has been revealed through topographic maps, which show variations in neuron density across the retina. Although topographic maps of about 300 vertebrates are available, there is no method for characterizing retinal traits quantitatively. Our goal is to present a novel method to standardize information on the position of the retinal specializations and changes in retinal ganglion cell (RGC) density across the retina from published topographic maps. We measured the position of the retinal specialization using two Cartesian coordinates and the gradient in cell density by sampling ganglion cell density values along four axes (nasal, temporal, ventral, and dorsal). Using this information, along with the peak and lowest RGC densities, we conducted discriminant function analyses (DFAs) to establish if this method is sensitive to distinguish three common types of retinal specializations (fovea, area, and visual streak). The discrimination ability of the model was higher when considering terrestrial (78%-80% correct classification) and aquatic (77%-86% correct classification) species separately than together. Our method can be used in the future to test specific hypotheses on the differences in retinal morphology between retinal specializations and the association between retinal morphology and behavioral and ecological traits using comparative methods controlling for phylogenetic effects