Search for: All records

Abstract Evolutionary biologists characterize macroevolutionary trends of phenotypic change across the tree of life using phylogenetic comparative methods. However, within‐species variation can complicate such investigations. For this reason, procedures for incorporating nonstructured (random) intraspecific variation have been developed.Likewise, evolutionary biologists seek to understand microevolutionary patterns of phenotypic variation within species, such as sex‐specific differences or allometric trends. Additionally, there is a desire to compare such within‐species patterns across taxa, but current analytical approaches cannot be used to interrogate within‐species patterns while simultaneously accounting for phylogenetic non‐independence. Consequently, deciphering how intraspecific trends evolve remains a challenge.Here we introduce an extended phylogenetic generalized least squares (E‐PGLS) procedure which facilitates comparisons of within‐species patterns across species while simultaneously accounting for phylogenetic non‐independence.Our method uses an expanded phylogenetic covariance matrix, a hierarchical linear model, and permutation methods to obtain empirical sampling distributions and effect sizes for model effects that can evaluate differences in intraspecific trends across species for both univariate and multivariate data, while conditioning them on the phylogeny.The method has appropriate statistical properties for both balanced and imbalanced data. Additionally, the procedure obtains evolutionary covariance estimates that reflect those from existing approaches for nonstructured intraspecific variation. Importantly, E‐PGLS can detect differences in structured (i.e. microevolutionary) intraspecific patterns across species when such trends are present. Thus, E‐PGLS extends the reach of phylogenetic comparative methods into the intraspecific comparative realm, by providing the ability to compare within‐species trends across species while simultaneously accounting for shared evolutionary history. 

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. 

Abstract Sexual dimorphism (SD) is a common feature of animals, and selection for sexually dimorphic traits may affect both functional morphological traits and organismal performance. Trait evolution through natural selection can also vary across environments. However, whether the evolution of organismal performance is distinct between the sexes is rarely tested in a phylogenetic comparative context. Anurans commonly exhibit sexual size dimorphism, which may affect jumping performance given the effects of body size on locomotion. They also live in a wide variety of microhabitats. Yet the relationships among dimorphism, performance, and ecology remain underexamined in anurans. Here, we explore relationships between microhabitat use, body size, and jumping performance in males and females to determine the drivers of dimorphic patterns in jumping performance. Using methods for predicting jumping performance through anatomical measurements, we describe how fecundity selection and natural selection associated with body size and microhabitat have likely shaped female jumping performance. We found that the magnitude of sexual size dimorphism (where females are about 14% larger than males) was much lower than dimorphism in muscle volume, where females had 42% more muscle than males (after accounting for body size). Despite these sometimes‐large averages, phylogenetict‐tests failed to show the statistical significance of SD for any variable, indicating sexually dimorphic species tend to be closely related. While SD of jumping performance did not vary among microhabitats, we found female jumping velocity and energy differed across microhabitats. Overall, our findings indicate that differences in sex‐specific reproductive roles, size, jumping‐related morphology, and performance are all important determinants in how selection has led to the incredible ecophenotypic diversity of anurans.