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Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate high-dimensional datasets. The approach is derived from the statistical equivalency between parametric methods using covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation, and phenotypic change in high-dimensional datasets.

Phenotypic convergence across distantly related taxa can be driven by similar selective pressures from the environment or intrinsic constraints. The roles of these processes on physiological strategies, such as homeothermy, are poorly understood. We studied the evolution of thermal properties of mammalian pelage in a diverse community of rodents inhabiting the Mojave Desert, USA. We used a heat flux device to measure the thermal insulation of museum specimens and determined whether thermal properties were associated with habitat preferences while assessing phylogenetic dependence. Species that prefer arid habitats exhibited lower conductivity and thinner pelage relative to species with other habitat preferences. Despite being thinner, the pelage of arid species exhibited comparable insulation to the pelage of the other species due to its lower conductivity. Thus, arid species have insulative pelage while simultaneously benefitting from thin pelage that promotes convective cooling. We found no evidence of intrinsic constraints or phylogenetic dependence, indicating pelage readily evolves to environmental pressures. Thermoregulatory simulations demonstrated that arid specialists reduced energetic costs required for homeothermy by 14.5% by evolving lower conductivity, providing support for adaptive evolution of pelage. Our study indicates that selection for lower energetic requirements of homeothermy has shaped evolution of pelage thermal properties.

Evolutionary biologists have long been fascinated with the episodes of rapid phenotypic innovation that underlie the emergence of major lineages. Although our understanding of the environmental and ecological contexts of such episodes has steadily increased, it has remained unclear how population processes contribute to emergent macroevolutionary patterns. One insight gleaned from phylogenomics is that gene-tree conflict, frequently caused by population-level processes, is often rampant during the origin of major lineages. With the understanding that phylogenomic conflict is often driven by complex population processes, we hypothesized that there may be a direct correspondence between instances of high conflict and elevated rates of phenotypic innovation if both patterns result from the same processes. We evaluated this hypothesis in six clades spanning vertebrates and plants. We found that the most conflict-rich regions of these six clades also tended to experience the highest rates of phenotypic innovation, suggesting that population processes shaping both phenotypic and genomic evolution may leave signatures at deep timescales. Closer examination of the biological significance of phylogenomic conflict may yield improved connections between micro- and macroevolution and increase our understanding of the processes that shape the origin of major lineages across the Tree of Life.

What is the nature of the genetic changes underlying phenotypic evolution? We have catalogued 1008 alleles described in the literature that cause phenotypic differences among animals, plants, and yeasts. Surprisingly, evolution of similar traits in distinct lineages often involves mutations in the same gene (\"gene reuse\"). This compilation yields three important qualitative implications about repeated evolution. First, the apparent evolution of similar traits by gene reuse can be traced back to two alternatives, either several independent causative mutations or a single original mutational event followed by sorting processes. Second, hotspots of evolution—defined as the repeated occurrence of de novo mutations at orthologous loci and causing similar phenotypic variation—are omnipresent in the literature with more than 100 examples covering various levels of analysis, including numerous gain-of-function events. Finally, several alleles of large effect have been shown to result from the aggregation of multiple small-effect mutations at the same hotspot locus, thus reconciling micromutationist theories of adaptation with the empirical observation of large-effect variants. Although data heterogeneity and experimental biases prevented us from extracting quantitative trends, our synthesis highlights the existence of genetic paths of least resistance leading to viable evolutionary change.

Evolutionary morphologists frequently wish to understand the extent to which organisms are integrated, and whether the strength of morphological integration among subsets of phenotypic variables differ among taxa or other groups. However, comparisons of the strength of integration across datasets are difficult, in part because the summary measures that characterize these patterns (RV coefficient and rPLS) are dependent both on sample size and on the number of variables. As a solution to this issue, we propose a standardized test statistic (a z-score) for measuring the degree of morphological integration between sets of variables. The approach is based on a partial least squares analysis of trait covariation, and its permutation-based sampling distribution. Under the null hypothesis of a random association of variables, the method displays a constant expected value and confidence intervals for datasets of differing sample sizes and variable number, thereby providing a consistent measure of integration suitable for comparisons across datasets. A two-sample test is also proposed to statistically determine whether levels of integration differ between datasets, and an empirical example examining cranial shape integration in Mediterranean wall lizards illustrates its use. Some extensions of the procedure are also discussed.

Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (Kmult) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of Kmult remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in highdimensional data. Statistical properties of Kmult were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders.

Frogs and toads (Amphibia: Anura) display diverse ecologies and behaviours, which are often correlated with visual capacity in other vertebrates. Additionally, anurans exhibit a broad range of relative eye sizes, which have not previously been linked to ecological factors in this group. We measured relative investment in eye size and corneal size for 220 species of anurans representing all 55 currently recognized families and tested whether they were correlated with six natural history traits hypothesized to be associated with the evolution of eye size. Anuran eye size was significantly correlated with habitat, with notable decreases in eye investment among fossorial, subfossorial and aquatic species. Relative eye size was also associated with mating habitat and activity pattern. Compared to other vertebrates, anurans have relatively large eyes for their body size, indicating that vision is probably of high importance. Our study reveals the role that ecology and behaviour may have played in the evolution of anuran visual systems and highlights the usefulness of museum specimens, and importance of broad taxonomic sampling, for interpreting macroecological patterns.

The study of modularity is paramount for understanding trends of phenotypic evolution, and for determining the extent to which covariation patterns are conserved across taxa and levels of biological organization. However, biologists currently lack quantitative methods for statistically comparing the strength of modular signal across datasets, and a robust approach for evaluating alternative modular hypotheses for the same dataset. As a solution to these challenges, we propose an effect size measure (ZCR ) derived from the covariance ratio, and develop hypothesis-testing procedures for their comparison. Computer simulations demonstrate that ZCR displays appropriate statistical properties and low levels of mis-specification, implying that it correctly identifies modular signal, when present. By contrast, alternative methods based on likelihood (EMMLi) and goodness of fit (MINT) suffer from high false positive rates and high model mis-specification rates. An empirical example in sigmodontine rodent mandibles is provided to illustrate the utility of ZCR for comparing modular hypotheses. Overall, we find that covariance ratio effect sizes are useful for comparing patterns of modular signal across datasets or for evaluating alternative modular hypotheses for the same dataset. Finally, the statistical philosophy for pairwise model comparisons using effect sizes should accommodate any future analytical developments for characterizing modular signal.

Morphological stasis or the absence of morphological change is a well-known phenomenon in the paleontological record, yet it is poorly integrated with neontological evidence. Recent evidence suggests that cryptic species complexes may remain morphologically identical due to morphological stasis. Here, we describe a case of long-term stasis in the Stygocapitella cryptic species complex (Parergodrilidae, Orbiniida, Annelida). Using phylogenetic methods and morphological data, we find that rates of morphological evolution in Stygocapitella are significantly slower than in closely related taxa (Nerillidae, Orbiniidae). Assessment of quantitative and qualitative morphology revealed the presence of four morphotypes with only subtle differences, whereas molecular data supports 10 reproductively isolated clades. Notably, estimates for the time of Stygocapitella species divergence range from ~275 million years to ~18 million years, including one case of two morphologically similar species that have diverged about 140 million years ago. These findings provide evidence for morphological deceleration and long-term morphological stasis in Stygocapitella, and that speciation is not necessarily accompanied by morphological changes. The deceleration of morphological divergence in Stygocapitella can be potentially linked to niche conservatism and tracking, coupled with the fluctuating dynamics of the interstitial environment, or genetic constraints due to progenetic evolution. Finally, we conclude that failing to integrate speciation without morphological evolution in paleontology may bias estimates of rates of speciation and morphological evolution.