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Species Tree Inference

A Guide to Methods and Applications

L. Lacey Knowles (Hrsg.), Laura Kubatko (Hrsg.)

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Naturwissenschaften, Medizin, Informatik, Technik / Naturwissenschaften allgemein


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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Conditional probability distribution, Probability, Addition, Approximation, Gene duplication, Amplicon, Introgression, Result, Probability distribution, Species, Allele, Estimation, Narration, Normal distribution, Phylogenetic network, Phylogenomics, Nucleotide, Confidence interval, Ammunition, Parameter, Solanum, Data set, Lycopersicon, Order of magnitude, Speciation, Rational number, Network topology, Reticulation (single-access key), NP-hardness, Posterior probability, Likelihood function, Shading, Evolution, Pseudolikelihood, Error term, Scalability, Null hypothesis, Inference, Taxon, Prediction, Sequence alignment, Error, Identifiability, Subset, Polyploid, Bayesian inference, Uncertainty, Markov chain Monte Carlo, Hybrid (biology), Sample Size, Implementation, Trade-off, Gene, Algorithm, Common descent, Outgroup (cladistics), Linear regression, Computational phylogenetics, Requirement, Test statistic, Accuracy and precision, Horizontal gene transfer, F1 hybrid, Computation, CPU time, Gene flow, Tax, Graphics processing unit, Coalescent theory, Phylogenetics