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Abstract Vision is a complex sensory system that requires coordination among cellular and morphological traits, and it remains unclear how functional relationships among traits interact with ecological selective pressures to shape the evolution of vision. Many species have specialized high visual acuity regions in the retina defined by patterns of ganglion cell density, which may evolve in response to ecological traits. For example, ganglion cell density can increase radially towards the center of the retina to form an area centralis, which is thought to improve acuity towards the center of the visual field in predators. Another example is the horizontal streak, where ganglion cells are dense in a horizontal pattern across the retina, which is thought to be beneficial in horizon-dominated habitats. At the morphological level, many have proposed that predation selects for high orbit convergence angles, or forward-facing eyes. We tested these hypotheses in a phylogenetic framework across eutherian mammals and found support for the association between the horizontal streak and horizon-dominated habitats. However, we did not find a significant association between orbit convergence and predation. We also tested if retinal specializations evolve in response to orbit convergence angles. We found that horizontal streaks were associated with side-facing eyes, potentially facilitating panoramic vision. Previous studies observed that some species with side-facing eyes have an area centralis shifted towards the temporal side of the retina, such that the high acuity region would project forward, but this relationship had not been tested quantitatively. We found that the temporal distance of the area centralis from the center of the retina was inversely correlated with orbit convergence, as predicted. Our work shows a strong relationship between orbit convergence and retinal specializations. We find support that both visual ecology and functional interactions among traits play important roles in the evolution of ocular traits across mammals. 

Abstract Comparative genomics approaches seek to associate molecular evolution with the evolution of phenotypes across a phylogeny. Many of these methods lack the ability to analyze non-ordinal categorical traits with more than two categories. To address this limitation, we introduce an expansion to RERconverge that associates shifts in evolutionary rates with the convergent evolution of categorical traits. The categorical RERconverge expansion includes methods for performing categorical ancestral state reconstruction, statistical tests for associating relative evolutionary rates with categorical variables, and a new method for performing phylogeny-aware permutations, “permulations”, on categorical traits. We demonstrate our new method on a three-category diet phenotype, and we compare its performance to binary RERconverge analyses and two existing methods for comparative genomic analyses of categorical traits: phylogenetic simulations and a phylogenetic signal based method. We present an analysis of how the categorical permulations scale with the number of species and the number of categories included in the analysis. Our results show that our new categorical method outperforms phylogenetic simulations at identifying genes and enriched pathways significantly associated with the diet phenotypes and that the categorical ancestral state reconstruction drives an improvement in our ability to capture diet-related enriched pathways compared to binary RERconverge when implemented without user input on phenotype evolution. The categorical expansion to RERconverge will provide a strong foundation for applying the comparative method to categorical traits on larger data sets with more species and more complex trait evolution than have previously been analyzed. 

Vocal production learning (“vocal learning”) is a convergently evolved trait in vertebrates. To identify brain genomic elements associated with mammalian vocal learning, we integrated genomic, anatomical, and neurophysiological data from the Egyptian fruit bat (Rousettus aegyptiacus) with analyses of the genomes of 215 placental mammals. First, we identified a set of proteins evolving more slowly in vocal learners. Then, we discovered a vocal motor cortical region in the Egyptian fruit bat, an emergent vocal learner, and leveraged that knowledge to identify active cis-regulatory elements in the motor cortex of vocal learners. Machine learning methods applied to motor cortex open chromatin revealed 50 enhancers robustly associated with vocal learning whose activity tended to be lower in vocal learners. Our research implicates convergent losses of motor cortex regulatory elements in mammalian vocal learning evolution.