Species Tree Inference: A Guide to Methods and Applications - Softcover

 
9780691207605: Species Tree Inference: A Guide to Methods and Applications

Inhaltsangabe

An up-to-date reference book on phylogenetic methods and applications for evolutionary biologists

The increasingly widespread availability of genomic data is transforming how biologists estimate evolutionary relationships among organisms and broadening the range of questions that researchers can test in a phylogenetic framework. Species Tree Inference brings together many of today’s leading scholars in the field to provide an incisive guide to the latest practices for analyzing multilocus sequence data.

This wide-ranging and authoritative book gives detailed explanations of emerging new approaches and assesses their strengths and challenges, offering an invaluable context for gauging which procedure to apply given the types of genomic data and processes that contribute to differences in the patterns of inheritance across loci. It demonstrates how to apply these approaches using empirical studies that span a range of taxa, timeframes of diversification, and processes that cause the evolutionary history of genes across genomes to differ.

By fully embracing this genomic heterogeneity, Species Tree Inference illustrates how to address questions beyond the goal of estimating phylogenetic relationships of organisms, enabling students and researchers to pursue their own research in statistically sophisticated ways while charting new directions of scientific discovery.

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Über die Autorin bzw. den Autor

Laura S. Kubatko is Professor of Statistics and of Evolution, Ecology, and Organismal Biology at The Ohio State University. L. Lacey Knowles is the Robert B. Payne Collegiate Professor of Ecology and Evolutionary Biology and Curator of Insects at the University of Michigan. They are the coeditors of Estimating Species Trees: Practical and Theoretical Aspects.

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9780691207599: Species Tree Inference: A Guide to Methods and Applications

Vorgestellte Ausgabe

ISBN 10:  0691207593 ISBN 13:  9780691207599
Verlag: Princeton University Press, 2023
Hardcover