Laura Kubatko & L. Lacey Knowles 
Species Tree Inference [EPUB ebook] 
A Guide to Methods and Applications

Apoio

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.

€52.99
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Sobre o 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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Língua Inglês ● Formato EPUB ● Páginas 352 ● ISBN 9780691245157 ● Tamanho do arquivo 43.1 MB ● Editor Laura Kubatko & L. Lacey Knowles ● Editora Princeton University Press ● Cidade Princeton ● País US ● Publicado 2023 ● Carregável 24 meses ● Moeda EUR ● ID 8540933 ● Proteção contra cópia Adobe DRM
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