01 / Reading Guide
Reading Guide
Who this book is for
For advanced undergraduates and graduate readers learning comparative evolutionary analysis. The book moves beyond interpreting a tree as a picture of relationships to treating shared ancestry as part of a statistical model. It is useful for students who want to understand what comparative data can tell us about evolutionary processes and which conclusions depend on untested assumptions about trees, traits and sampling.
Prerequisites
An introductory evolution course, probability, regression and basic statistical model fitting. Comfort with logarithms and likelihood is helpful; computational examples benefit from familiarity with R and phylogenetic data.
What it covers
Statistical modelling leads into Brownian motion, multivariate trait evolution and alternative models. Discrete character evolution, birth-death processes and diversification receive parallel conceptual and model-fitting treatment. The progression connects biological questions with the probability models used to express them, rather than presenting software commands as a substitute for reasoning.
How to use it
Start with a simple tree and explain why related species are not independent observations. Simulate traits under a known model before fitting the same model to empirical data. Record the meaning of each parameter, inspect model assumptions and compare an alternative process. Repeat an analysis across plausible trees or sampling choices and distinguish uncertainty in the tree from uncertainty in model parameters. Explain what a good fit supports and what it cannot establish about evolutionary history.
Edition and sources
The author provides the complete online text and PDF with a 2019 dated text and CC BY terms. This is the conceptual comparative-methods work, distinct from the later jointly authored Phylogenetic Comparative Methods in R. No print-format ISBN is attached without direct version confirmation.
