Estimating Species Trees by L. Lacey Knowles
Practical and Theoretical Aspects

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Recent computational and modeling advances have produced methods for estimating species trees directly, avoiding the problems and limitations of the traditional phylogenetic paradigm where an estimated gene tree is equated with the history of species divergence. The overarching goal of the volume is to increase the visibility and use of these new methods by the entire phylogenetic community by specifically addressing several challenges: (i) firm understanding of the theoretical underpinnings of the methodology, (ii) empirical examples demonstrating the utility of the methodology as well as its limitations, and (iii) attention to technical aspects involved in the actual software implementation of the methodology. As such, this volume will not only be poised to become the quintessential guide to training the next generation of researchers, but it will also be instrumental in ushering in a new phylogenetic paradigm for the 21st century.

About L. Lacey Knowles

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L. Lacey Knowles, Ph.D., is an Associate Professor/Associate Curator for the Museum of Zoology at the University of Michigan. Her research areas include speciation, sexual selection, phylogeography, and evolutionary radiations. Dr. Knowles was recently awarded a three-year grant by The National Science Foundation, titled "Population genetics of species delimitation: Methodology and application of a unified approach to inferring species boundaries."Laura S. Kubatko, Ph.D., is an Associate Professor in the Departments of Statistics and Evolution, Ecology, and Organismal Biology. Her research interests are in statistical genetics, including the estimation of phylogenetic trees from nucleotide sequence data, linkage and QTL analysis, and the analysis of microarray data. She recently became an Associate Editor for the journal Systematic Biology, and was also elected to the Council for the Society of Systematic Biology beginning in 2008.
Published May 9, 2011 by Wiley-Blackwell. 232 pages
Genres: Nature & Wildlife, Science & Math, Professional & Technical. Non-fiction

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