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Abstract Many hypotheses in the field of phylogenetic comparative biology involve specific changes in the rate or process of trait evolution. This is particularly true of approaches designed to connect macroevolutionary pattern to microevolutionary process. We present a method designed to test whether the rate of evolution of a discrete character has changed in one or more clades, lineages, or time periods. This method differs from other related approaches (such as the ‘covarion’ model) in that the ‘regimes’ in which the rate or process is postulated to have changed are specified a priori by the user, rather than inferred from the data. Similarly, it differs from methods designed to model a correlation between two binary traits in that the regimes mapped onto the tree are fixed. We apply our method to investigate the rate of dewlap color and/or caudal vertebra number evolution in Caribbean and mainland clades of the diverse lizard genus Anolis. We find little evidence to support any difference in the evolutionary process between mainland and island evolution for either character. We also examine the statistical properties of the method more generally and show that it has acceptable type I error, parameter estimation, and power. Finally, we discuss some general issues of frequentist hypothesis testing and model adequacy, as well as the relationship of our method to existing models of heterogeneity in the rate of discrete character evolution on phylogenies. 

Abstract Here, I briefly present a new R package calledlearnPopGenthat has been designed primarily for the purposes of teaching evolutionary biology, population genetics, and evolutionary theory. Functions of the package can be used to conduct simulations and numerical analyses of a wide range of evolutionary phenomena that would typically be covered in advanced undergraduate through graduate‐level curricula in population genetics or evolution. For instance,learnPopGenfunctions can be used to visualize gene frequency changes through time under multiple deterministic and stochastic processes, to compute and animate the changes in phenotypic trait values or distributions under natural selection, to numerically analyze and graph the outcome of simple game theory models, and to plot coalescence within a population experiencing genetic drift, along with a number of other things. Functions have been designed to be maximally didactic and frequently employ compelling animated visualizations. Furthermore, it is straightforward to export plots and animations from R in the form of flat or animated graphics, or as videos. For maximum flexibility, students working with the package can run functions directly in R; however, instructors may choose to guide students less adept in the R environment to one of various web interfaces that I have built for a number of the functions of the package and that are already available online. 

Phylogenetic comparative methods comprise the general endeavor of using an estimated phylogenetic tree (or set of trees) to make secondary inferences: about trait evolution, diversification dynamics, biogeography, community ecology, and a wide range of other phenomena or processes. Over the past ten years or so, thephytoolsR package has grown to become an important research tool for phylogenetic comparative analysis.phytoolsis a diverse contributed R library now consisting of hundreds of different functions covering a variety of methods and purposes in phylogenetic biology. As of the time of writing,phytoolsincluded functionality for fitting models of trait evolution, for reconstructing ancestral states, for studying diversification on trees, and for visualizing phylogenies, comparative data, and fitted models, as well numerous other tasks related to phylogenetic biology. Here, I describe some significant features of and recent updates tophytools, while also illustrating several popular workflows of thephytoolscomputational software. 

Numerous questions in phylogenetic comparative biology revolve around the correlated evolution of two or more phenotypic traits on a phylogeny. In many cases, it may be sufficient to assume a constant value for the evolutionary correlation between characters across all the clades and branches of the tree. Under other circumstances, however, it is desirable or necessary to account for the possibility that the evolutionary correlation differs through time or in different sections of the phylogeny. Here, we present a method designed to fit a hierarchical series of models for heterogeneity in the evolutionary rates and correlation of two quantitative traits on a phylogenetic tree. We apply the method to two datasets: one for different attributes of the buccal morphology in sunfishes (Centrarchidae); and a second for overall body length and relative body depth in rock- and non-rock-dwelling South American iguanian lizards. We also examine the performance of the method for parameter estimation and model selection using a small set of numerical simulations. 

Body sizes of marine amniotes span six orders of magnitude, yet the factors that governed the evolution of this diversity are largely unknown. High primary production of modern oceans is considered a prerequisite for the emergence of cetacean giants, but that condition cannot explain gigantism in Triassic ichthyosaurs. We describe the new giant ichthyosaur Cymbospondylus youngorum sp. nov. with a 2-meter-long skull from the Middle Triassic Fossil Hill Fauna of Nevada, USA, underscoring rapid size evolution despite the absence of many modern primary producers. Surprisingly, the Fossil Hill Fauna rivaled the composition of modern marine mammal faunas in terms of size range, and energy-flux models suggest that Middle Triassic marine food webs were able to support several large-bodied ichthyosaurs at high trophic levels, shortly after ichthyosaur origins.