# Browsing by Subject "Phylogenetic trees"

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Item Estimating phylogenetic trees from discrete morphological data(2015-05) Wright, April Marie; Hillis, David M., 1958-; Cannatella, David C; Jansen, Robert K; Linder, Craig R; Smith, Martha KShow more Morphological characters have a long history of use in the estimation of phylogenetic trees. Datasets consisting of morphological characters are most often analyzed using the maximum parsimony criterion, which seeks to minimize the amount of character change across a phylogenetic tree. When combined with molecular data, characters are often analyzed using model-based methods, such as maximum likelihood or, more commonly, Bayesian estimation. The efficacy of likelihood and Bayesian methods using a common model for estimating topology from discrete morphological characters, the Mk model, is poorly-explored. In Chapter One, I explore the efficacy of Bayesian estimation of phylogeny, using the Mk model, under conditions that are commonly encountered in paleontological studies. Using simulated data, I describe the relative performances of parsimony and the Mk model under a range of realistic conditions that include common scenarios of missing data and rate heterogeneity. I further examine the use of the Mk model in Chapter Two. Like any model, the Mk model makes a number of assumptions. One is that transition between character states are symmetric (i.e., there is an equal probability of changing from state 0 to state 1 and from state 1 to state 0). Many characters, including alleged Dollo characters and extremely labile characters, may not fit this assumption. I tested methods for relaxing this assumption in a Bayesian context. Using empirical datasets, I performed model fitting to demonstrate cases in which modelling asymmetric transitions among characters is preferred. I used simulated datasets to demonstrate that choosing the best-fit model of transition state symmetry can improve model fit and phylogenetic estimation. In my final chapter, I looked at the use of partitions to model datasets more appropriately. Common in molecular studies, partitioning breaks up the dataset into pieces that evolve according to similar mechanisms. These pieces, called partitions, are then modeled separately. This practice has not been widely adopted in morphological studies. I extended the PartitionFinder software, which is used in molecular studies to score different possible partition schemes to find the one which best models the dataset. I used empirical datasets to demonstrate the effects of partitioning datasets on model likelihoods and on the phylogenetic trees estimated from those datasets.Show more Item Phylogenetic supertree methods(2009-05) Swenson, Michelle Dawn; Warnow, Tandy, 1955-; Linder, C. RandalShow more The central task in phylogenetics is to infer the evolutionary relationships among a given set of species. These relationships are usually represented by a phylogenetic tree with the species of interest at the leaves and where the internal vertices of the tree represent ancestral species. The amount of available molecular data is increasing exponentially and, given the continual advances in sequencing techniques and throughput, this explosive growth will likely continue. These vast amounts of available data mean that biologists are able to assemble large multi-gene datasets for use in phylogenetic analyses, which presents distinct computational challenges. Supertree methods comprise one approach to reconstructing large phylogenies, given estimated trees for overlapping subsets of the entire set of taxa. These source trees are combined into a single supertree on the full set of taxa using various algorithmic techniques. When the data allow, the competing approach is a combined analysis (also known as a “super-matrix” or “total evidence” approach), whereby the different sequence data matrices for each of the different subsets of taxa are put into a single super-matrix, and a tree is estimated on that super-matrix. In this dissertation, I present simulation software I designed to allow users to compare the relative performance of different supertree methods, as well as that of combined analysis, on more realistic data and on a larger scale than has been used up to this point. I present an extensive simulation study that uses this software to compare the performance of supertree methods and combined analysis, and that demonstrates a need for more topologically accurate supertree methods. I also introduce a new supertree method that I have developed that outperforms the most commonly used, and what until now has arguably been the most accurate, supertree method.Show more