Rates of phenotypic evolution have changed throughout the history of life, producing variation in levels of morphological, functional, and ecological diversity among groups. Testing for the presence of these rate shifts is a key component of evaluating hypotheses about what causes them. In this paper, general predictions regarding changes in phenotypic diversity as a function of evolutionary history and rates are developed, and tests are derived to evaluate rate changes. Simulations show that these tests are more powerful than existing tests using standardized contrasts. The new approaches are distributed in an application called Brownie and in r8s.
Abstract. Rates of phenotypic evolution have changed throughout the history of life, producing variation in levels of morphological, functional, and ecological diversity among groups. Testing for the presence of these rate shifts is a key component of evaluating hypotheses about what causes them. In this paper, general predictions regarding changes in phenotypic diversity as a function of evolutionary history and rates are developed, and tests are derived to evaluate rate changes. Simulations show that these tests are more powerful than existing tests using standardized contrasts. All five extant flamingo species are long-legged filter feeders, whereas their sister group, consisting of twenty species of grebes (Van Tuinen et al. 2001;Chubb 2004;Mayr 2004), feed on prey ranging from fish and squid to minute invertebrates (Fjeldså 1983), and show a variety of body and bill shapes. Methods to test whether the difference in species number between flamingos and grebes arose by chance or reflects differences in diversification rates have been developed (Slowinski and Guyer 1989;Nee et al. 1992;Hey 1992;Harvey et al. 1994). These methods are aimed at discovering factors affecting diversification. But there are also undoubtedly factors that led to the difference in variability of ecologically important traits within these two groups. This paper is concerned with hypotheses about factors that lead to differences between groups in phenotypic and biological diversity, as opposed to species richness.There are many potential hypotheses regarding factors that can affect the rate of evolution of phenotypic characters (which include morphological, behavioral, physiological, biochemical, and ecological traits). For example, once wings replaced legs as the primary means of locomotion in birds, newly less constrained legs may have begun to evolve new shapes more rapidly (Gatesy and Middleton 1997). The evolution of asexuality may reduce the rate of genome size evolution. The invasion of a new, competitor-free island may increase the rate of evolution of feeding structures. These hypotheses all attempt to relate a change in some aspect of the biology of the lineage with a change of the rate of evolution of a continuous character based on an idea about how evolution works. Hypotheses can also be generated from observations of patterns of diversity instead of predictions based on a mechanism. Grebes appear to have more interspecific variation in bill dimensions than flamingos: this may reflect a faster rate of bill evolution, or perhaps the grebe species have been evolving independently for more time than the flamingo species.In this paper, we develop and implement new methods to make inferences regarding these questions. Basic results concerning character evolution on trees are presented. Our methods are illustrated using an example of genome size evolution in angiosperms. SOME BASIC PROPERTIES OF CHARACTER EVOLUTION ON TREESDisparity is commonly measured as variance of the states of the taxa (so higher disparity means the taxa are ...
We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagel's lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression.
Phylogenetic networks are necessary to represent the tree of life expanded by edges to represent events such as horizontal gene transfers, hybridizations or gene flow. Not all species follow the paradigm of vertical inheritance of their genetic material. While a great deal of research has flourished into the inference of phylogenetic trees, statistical methods to infer phylogenetic networks are still limited and under development. The main disadvantage of existing methods is a lack of scalability. Here, we present a statistical method to infer phylogenetic networks from multi-locus genetic data in a pseudolikelihood framework. Our model accounts for incomplete lineage sorting through the coalescent model, and for horizontal inheritance of genes through reticulation nodes in the network. Computation of the pseudolikelihood is fast and simple, and it avoids the burdensome calculation of the full likelihood which can be intractable with many species. Moreover, estimation at the quartet-level has the added computational benefit that it is easily parallelizable. Simulation studies comparing our method to a full likelihood approach show that our pseudolikelihood approach is much faster without compromising accuracy. We applied our method to reconstruct the evolutionary relationships among swordtails and platyfishes (Xiphophorus: Poeciliidae), which is characterized by widespread hybridizations.
BUCKy is open source and distributed under the GNU general public license at www.stat.wisc.edu/∼ane/bucky/.
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