Data set: In order to sample all felid species, we took advantage of public databases. Images of individuals from 40 extant felid species (all but Felis catus, excluded due to the artificial selection on eye color in domesticated cats by humans), as well as 13 identifiable subspecies and the banded linsang as an outgroup (Prionodon linsang), were found using Google Images and iNaturalist using both the scientific name and the common name for each species as search terms. This approach, taking advantage of the enormous resource of publicly available images, allows access to a much larger data set than in the published scientific literature or that would be possible to obtain de novo for this study. Public image-based methods for character state classification have been used previously, such as in a phylogenetic analysis of felid coat patterns (Werdelin and Olsson 1997). However, this approach does require implementing strong criteria for selecting images. Criteria used to choose images included selecting images where the animal was facing toward the camera, at least one eye was unobstructed, the animal was a non-senescent adult, and the eye was not in direct light, causing glare, or completely in shadow, causing unwanted darkening. The taxonomic identity of the animal in each selected image was verified through images present in the literature, as well as the "research grade" section of iNaturalist. When possible, we collected five images per taxon, although some rarer taxa had fewer than five acceptable images available. In addition, some species with a large number of eye colors needed more than five images to capture their variation, determined by quantitative methods discussed below. Once the images were selected, they were manually edited using MacOS Preview. This editing process involved choosing the "better" of the two eyes for each felid image (i.e. the one that is most visible and with the least glare and shadow). Then, the section of the iris for that eye without obstruction, such as glare, shadow, or fur, was cropped out. This process resulted in a data set of 269 cropped, standardized, felid irises. Eye color identification: To impartially identify the eye color(s) present in each felid population, the data set images were loaded by species into Python (version 3.8.8) using the Python Imaging Library (PIL) (Van Rossum and Drake 2009; Clark 2015). For each image, the red, green, and blue (RGB) values for each of its pixels were extracted. Then, they were averaged and the associated hex color code for the average R, G, and B values was printed. The color associated with this code was identified using curated and open-source color identification programs (Aerne 2022; Cooper 2022). This data allowed the color of each eye in the data set to be correctly identified, removing a great deal of the bias inherent in a researcher subjectively deciding the color of each iris. Eye colors were assigned on this basis to one of five fundamental color groups: brown, hazel/green, yellow/beige, gray, and blue. To ensure no data was missed due to low sample size, the first 500 Google Images, as well as all the "research grade" images on iNaturalist, were viewed for each species. Any missed colors were added to the data set. This method nonetheless has a small, but non-zero, chance to miss rare eye colors that are present in species. However, overall, it provides a robust and repeatable way to identify the general iris colors present in animals. In addition, if, for a given species, one or two eye colors were greatly predominant in the available data online (>80% for one or ~40% for both, respectively), they were defined as being the most common eye color(s). With this assessment, the phylogenetic analysis below could be carried out both with all recorded eye colors and using only the most common eye colors, thereby assuring that rare eye colors did not skew the results. Shade measurements within each color group: For each species, the images were sorted into their groups by assigned color. For each group, RGB values for each pixel in each image were again extracted, resulting in a three-dimensional data set. This was reduced to two dimensions using Uniform Manifold Approximation and Projection (UMAP) (McInnes et al. 2018). The graph for each image was then analyzed using k-means clustering through the package scikit-learn (version 1.2.0) (Pedregosa et al. 2011). The number of clusters (k), indicating the number of distinct shades of color in the iris of each animal, was determined using elbow plots. After this was done for all images in the group, the k values were averaged and each image was clustered using the average k value, rounded to the nearest integer. This was done to standardize within groups, avoid confounders based on lower-quality images, and allow for comparative analysis. After this, the average RGB values for each cluster for each image were calculated. Then, the clusters were matched up based ...