A comparative method for studying adaptation to a randomly evolving environment.
ABSTRACT Most phylogenetic comparative methods used for testing adaptive hypotheses make evolutionary assumptions that are not compatible with evolution toward an optimal state. As a consequence they do not correct for maladaptation. The "evolutionary regression" that is returned is more shallow than the optimal relationship between the trait and environment. We show how both evolutionary and optimal regressions, as well as phylogenetic inertia, can be estimated jointly by a comparative method built around an Ornstein-Uhlenbeck model of adaptive evolution. The method considers a single trait adapting to an optimum that is influenced by one or more continuous, randomly changing predictor variables.
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ABSTRACT: 1. We present mvMORPH, a package of multivariate phylogenetic comparative methods for the R statistical environment. mvMORPH is freely available on the CRAN package repository (http://cran.r-project.org/web/packages/mvMORPH/). 2. mvMORPH allows fitting a range of multivariate evolutionary models under a maximum-likelihood criterion. Initially developed in the context of phylogenetic analysis of multiple morphometric traits, its use can be extended to any biological dataset with one or multiple covarying continuous traits. All the fitting models include the possibility to use SIMMAP-like mapping, which may be useful for fitting changes along lineages at a given point in time. All models provide diagnostic metrics for convergence and reliability of estimates, as well as the possibility to include trait measurement errors in model estimates. 3. New features provided by the mvMORPH package include the possibility of fitting models with changes in the mode of evolution along the phylogeny, which will be particularly meaningful in comparative analyses that include extinct taxa, e.g., when testing changes in evolutionary mode associated with global biotic/abiotic events. 4. We briefly describe the models already included in mvMORPH, and provide some demonstration of the use of the package with two simulated worked examples.Methods in Ecology and Evolution 06/2015; DOI:10.1111/2041-210X.12420 · 5.32 Impact Factor
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ABSTRACT: Most existing methods for modeling trait evolution are univariate, while researchers are often interested in investigating evolutionary patterns and processes across multiple traits. Principal components analysis (PCA) is commonly used to reduce the dimensionality of multivariate data as univariate trait models can be fit to the individual principal components. The problem with using standard PCA on phylogenetically structured data has been previously pointed out yet it continues to be widely used in the literature. Here we demonstrate precisely how using standard PCA can mislead inferences: the first few principal components of traits evolved under constant-rate multivariate Brownian motion will appear to have evolved via an "early burst" process. A phylogenetic PCA (pPCA) has been proprosed to alleviate these issues. However, when the true model of trait evolution deviates from the model assumed in the calculation of the pPCA axes, we find that the use of pPCA suffers from similar artifacts as standard PCA. We show that datasets with high effective dimensionality are particularly likely to lead to erroneous inferences. Ultimately, all of the problems we report stem from the same underlying issue-by considering only the first few principal components as univariate traits, we are effectively examining a biased sample of a multivariate pattern. These results highlight the need for truly multivariate phylogenetic comparative methods. As these methods are still being developed, we discuss potential alternative strategies for using and interpreting models fit to univariate axes of multivariate data. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: firstname.lastname@example.org.Systematic Biology 04/2015; DOI:10.1093/sysbio/syv019 · 11.53 Impact Factor
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ABSTRACT: Our aim is to understand the evolution of species-rich plant groups that shifted from tropical into cold/temperate biomes. It is well known that climate affects evolutionary processes, such as how fast species diversify, species range shifts, and species distributions. Many plant lineages may have gone extinct in the Northern Hemisphere due to Late Eocene climate cooling, while some tropical lineages may have adapted to temperate conditions and radiated; the hyper-diverse and geographically widespread genus Hypericum is one of these. To investigate the effect of macroecological niche shifts on evolutionary success we combine historical biogeography with analyses of diversification dynamics and climatic niche shifts in a phylogenetic framework. Hypericum evolved cold tolerance c. 30 million years ago, and successfully colonized all ice-free continents, where today ~500 species exist. The other members of Hypericaceae stayed in their tropical habitats and evolved into ~120 species. We identified a 15-20 million year lag between the initial change in temperature preference in Hypericum and subsequent diversification rate shifts in the Miocene. Contrary to the dramatic niche shift early in the evolution of Hypericum most extant species occur in temperate climates including high elevations in the tropics. These cold/temperate niches are a distinctive characteristic of Hypericum. We conclude that the initial release from an evolutionary constraint (from tropical to temperate climates) is an important novelty in Hypericum. However, the initial shift in the adaptive landscape into colder climates appears to be a precondition, and may not be directly related to increased diversification rates. Instead, subsequent events of mountain formation and further climate cooling may better explain distribution patterns and species-richness in Hypericum. These findings exemplify important macroevolutionary patterns of plant diversification during large-scale global climate change.BMC Evolutionary Biology 05/2015; 15:80. DOI:10.1186/s12862-015-0359-4 · 3.41 Impact Factor