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Abstract Evolutionary convergence in distantly related species is among the most convincing evidence of adaptive evolution. The mammalian ear, responsible for balance and hearing, is not only characterised by its spectacular evolutionary incorporation of several bones of the jaw, it also varies considerably in shape across modern mammals. Using a multivariate approach, we show that in Afrotheria, a monophyletic clade with morphologically and ecologically highly disparate species, inner ear shape has evolved similar adaptations as in non-afrotherian mammals. We identify four eco-morphological trait combinations that underlie this convergence. The high evolvability of the mammalian ear is surprising: Nowhere else in the skeleton are different functional units so close together; it includes the smallest bones of the skeleton, encapsulated within the densest bone. We suggest that this evolvability is a direct consequence of the increased genetic and developmental complexity of the mammalian ear compared to other vertebrates. 

Abstract Due to the hierarchical structure of the tree of life, closely related species often resemble each other more than distantly related species; a pattern termed phylogenetic signal. Numerous univariate statistics have been proposed as measures of phylogenetic signal for single phenotypic traits, but the study of phylogenetic signal for multivariate data, as is common in modern biology, remains challenging. Here, we introduce a new method to explore phylogenetic signal in multivariate phenotypes. Our approach decomposes the data into linear combinations with maximal (or minimal) phylogenetic signal, as measured by Blomberg’s K. The loading vectors of these phylogenetic components or K-components can be biologically interpreted, and scatterplots of the scores can be used as a low-dimensional ordination of the data that maximally (or minimally) preserves phylogenetic signal. We present algebraic and statistical properties, along with 2 new summary statistics, KA and KG, of phylogenetic signal in multivariate data. Simulation studies showed that KA and KG have higher statistical power than the previously suggested statistic Km⁢u⁢l⁢t, especially if phylogenetic signal is low or concentrated in a few trait dimensions. In 2 empirical applications to vertebrate cranial shape (crocodyliforms and papionins), we found statistically significant phylogenetic signal concentrated in a few trait dimensions. The finding that phylogenetic signal can be highly variable across the dimensions of multivariate phenotypes has important implications for current maximum likelihood approaches to phylogenetic signal in multivariate data.