Format: ORAL

Sarah Mathews

Department of Biological Sciences, Louisiana State University, Baton Rouge, USA

Analyses of morphological data sets are especially important when attempting to reconstruct phylogenies for clades whose diversity has been significantly reduced during historic periods of high extinction rates. Two such clades are seed plants, and one of its included clades, the cycads. These clades have evolutionary histories that increase the difficulty of accurate phylogenetic inference from molecular data: They are deeply diverged and extant members represent a fraction of their former diversity. In the case of cycads, of the forty described genera, just ten are extant. In seed plants, approximately 70% of its major lineages are extinct. Morphology thus provides a critical line of evidence in efforts to infer accurate phylogenies for these groups. In this study, we seek to address some of the challenges inherent in phylogenetic analyses of morphological data using recently assembled morphological data sets. First, different character sets within a matrix of living and fossil taxa may vary considerably in information content and may have discordant phylogenetic signals. We therefore are using a Bayesian approach to explore information content, and using Bayes Factors to explore discordance among characters and character sets that might lead to erroneous results and/or reduced resolution. Second, development of models of evolution for use in phylogenetic analyses of morphological data sets has lagged far behind that of models of nucleotide evolution. Recent advances have, however, enabled analyses to be conducted under more realistic assumptions about the evolution of morphological characters. We thus are inferring trees using Bayesian methods that allow different relaxed models of character evolution to be tested. I will discuss results from these analyses and from analyses that integrate morphological and slowly evolving molecular characters, which aim to obtain a synthetic understanding of seed plant evolution.