Elizabeth A. Housworth

ADAPTIVE CONSTRAINTS AND THE PHYLOGENETIC COMPARATIVE METHOD: A COMPUTER SIMULATION TEST

Abstract Recently, the utility of modern phylogenetic comparative methods (PCMs) has been questioned because of the seemingly restrictive assumptions required by these methods. Although most comparative analyses involve traits thought to be undergoing natural or sexual selection, most PCMs require an assumption that the traits be evolving by less directed random processes, such as Brownian motion (BM). In this study, we use computer simulation to generate data under more realistic evolutionary scenarios and consider the statistical abilities of a variety of PCMs to estimate correlation coefficients from these data. We found that correlations estimated without taking phylogeny into account were often quite poor and never substantially better than those produced by the other tested methods. In contrast, most PCMs performed quite well even when their assumptions were violated. Felsensteins independent contrasts (FIC) method gave the best performance in many cases, even when weak constraints had been acting throughout phenotypic evolution. When strong constraints acted in opposition to variance-generating (i.e., BM) forces, however, FIC correlation coefficients were biased in the direction of those BM forces. In most cases, all other PCMs tested (phylogenetic generalized least squares, phylogenetic mixed model, spatial autoregression, and phylogenetic eigenvector regression) yielded good statistical performance, regardless of the details of the evolutionary model used to generate the data. Actual parameter estimates given by different PCMs for each dataset, however, were occasionally very different from one another, suggesting that the choice among them should depend on the types of traits and evolutionary processes being considered. Corresponding Editor: J. Huelsenbeck

The Phylogenetic Mixed Model

The phylogenetic mixed model is an application of the quantitative‐genetic mixed model to interspecific data. Although this statistical framework provides a potentially unifying approach to quantitative‐genetic and phylogenetic analysis, the model has been applied infrequently because of technical difficulties with parameter estimation. We recommend a reparameterization of the model that eliminates some of these difficulties, and we develop a new estimation algorithm for both the original maximum likelihood and new restricted maximum likelihood estimators. The phylogenetic mixed model is particularly rich in terms of the evolutionary insight that might be drawn from model parameters, so we also illustrate and discuss the interpretation of the model parameters in a specific comparative analysis.

Phylogeny Shape and the Phylogenetic Comparative Method

We explored the impact of phylogeny shape on the results of interspecific statistical analyses incorporating phylogenetic information. In most phylogenetic comparative methods (PCMs), the phylogeny can be represented as a relationship matrix, and the hierarchical nature of interspecific phylogenies translates into a distinctive blocklike matrix that can be described by its eigenvectors (topology) and eigenvalues (branch lengths). Thus, differences in the eigenvectors and eigenvalues of different relationship matrices can be used to gauge the impact of possible phylogeny errors by comparing the actual phylogeny used in a PCM analysis with a second phylogenetic hypothesis that may be more accurate. For example, we can use the sum of inverse eigenvalues as a rough index to compare the impact of phylogenies with different branch lengths. Topological differences are better described by the eigenvectors. In general, phylogeny errors that involve deep splits in the phylogeny (e.g., moving a taxon across the base of the phylogeny) are likely to have much greater impact than will those involving small perturbations in the fine structure near the tips. Small perturbations, however, may have more of an impact if the phylogeny structure is highly dependent (with many recent splits near the tips of the tree). Unfortunately, the impact of any phylogeny difference on the results of a PCM depends on the details of the data being considered. Recommendations regarding the choice, design, and statistical power of interspecific analyses are also made.

Random Sampling of Constrained Phylogenies: Conducting Phylogenetic Analyses When the Phylogeny Is Partially Known

Statistical randomization tests in evolutionary biology often require a set of random, computer-generated trees. For example, earlier studies have shown how large numbers of computer-generated trees can be used to conduct phylogenetic comparative analyses even when the phylogeny is uncertain or unknown. These methods were limited, however, in that (in the absence of molecular sequence or other data) they allowed users to assume that no phylogenetic information was available or that all possible trees were known. Intermediate situations where only a taxonomy or other limited phylogenetic information (e.g., polytomies) are available are technically more difficult. The current study describes a procedure for generating random samples of phylogenies while incorporating limited phylogenetic information (e.g., four taxa belong together in a subclade). The procedure can be used to conduct comparative analyses when the phylogeny is only partially resolved or can be used in other randomization tests in which large numbers of possible phylogenies are needed.

A genomewide study of reproductive barriers between allopatric populations of a homosporous fern, Ceratopteris richardii.

Biological factors involved in reproductive barriers between two divergent races of Ceratopteris richardii were investigated. We used a combination of spore germination rates, QTL analysis of spore germination rates, and transmission ratio distortion (TRD) of 729 RFLPs, AFLPs, and isozyme markers distributed across the genome on the basis of hybrid populations of 488 doubled haploid lines (DHLs) and 168 F2s. Substantial reproductive barriers were found between the parental races, predominantly in the form of spore inviability (23.7% F1 spore viability). Intrinsic genetic factors such as Bateson–Dobzhansky–Muller (BDM) incompatibilities involving both nuclear–nuclear and nuclear–cytoplasmic factors and chromosomal rearrangements appear to contribute to intrinsic postzygotic isolation. The genomewide distribution patterns of TRD loci support the hypothesis that reproductive barriers are a byproduct of divergence in allopatry and that the strong reproductive barriers are attributable to a small number of genetic elements scattered throughout the genome.

Spatial genetics of wild tomato species reveals roles of the Andean geography on demographic history

PREMISE OF THE STUDY Understanding the demographic history of natural populations in relation to the geographic features in their habitats is an important step toward deciphering the mechanisms of evolutionary processes in nature. This study investigates how the complex geographic and ecological features of the Andes play a role in demographic history, species divergence, dispersal patterns, and hybridization in wild tomato species. METHODS We investigated spatial genetics of two closely related wild tomato species, Solanum lycopersicum and S. pimpinellifolium, by integrating amplified fragment length polymorphism (AFLP) marker data and geographic information system (GIS)-derived geographic and climatic data. KEY RESULTS The two species represent genetically distinct lineages largely separated by the Andes, but hybridize extensively in central to northern Ecuador, likely mediated by the transitional climatic conditions between those of the two species. Solanum lycospericum has likely experienced a severe population bottleneck during the colonization of the eastern Andes followed by a rapid population expansion. CONCLUSIONS The study demonstrates that the evolutionary patterns of the two wild tomatoes, including demographic history, dispersal patterns, interspecific divergence, and hybridization, are intimately related to the complex geographic and ecological features of the Andes. Integrating genetic data across the genome and GIS-derived environmental data can provide insights into the patterns of complex evolutionary processes in nature.

Methods for Analysis of Crossover Interference in Saccharomyces cerevisiae

Interest in crossover interference in yeast has been spurred by the discovery and characterization of mutants that alter it as well as by the development and testing of models to explain it. This chapter describes methods for detecting and for measuring interference, with emphasis on those that exploit the ability to examine all four products of individual acts of meiosis.

On the Number of Binary Characters Needed to Recover a Phylogeny Using Maximum Parsimony

We give an explicit construction to solve a conjecture of Mike Steel and David Penny that any phylogeny involving N taxa can be recovered unambiguously using on the order of log N binary characters and the method of maximum parsimony. Biologically, this means that homoplasy need not be a deterrent to parsimony methods. Some patterns of homoplasy are phylogenetically informative and can exponentially reduce the amount of data needed to resolve a phylogeny.

Is There Variation in Crossover Interference Levels Among Chromosomes From Human Males

We demonstrate that recent data from human males are consistent with constant interference levels among chromosomes under the two-pathway model, whereas inappropriately fitting shape parameters of Gamma distributions to immunofluorescent interfoci distances observed on finite chromosomes generates false interpretations of higher levels of interference on shorter chromosomes. We provide appropriate statistical methodology.

Phylogenetic ANCOVA: Estimating Changes in Evolutionary Rates as Well as Relationships between Traits

We present a new phylogenetic comparative method—phylogenetic analysis of covariance (PANCOVA)—that uses interspecific data and a phylogeny to estimate the effects of major events on both the rate of phenotypic evolution and the association between traits. It could be used, for example, to model the impact of a key innovation, colonization of a new habitat, or environmental change. The approach is optimized with maximum likelihood and is formulated under the familiar phylogenetic generalized least squares framework, which is flexible and easily extended to incorporate other factors and parameters. As an example, we explore the relationship between parental investment and relative telencephalon size in birds and contrast the results of PANCOVA with those from other phylogenetic comparative methods.