Latin American Macroevolution Workshop
Córdoba, Argentina. 1 August — 4 August, 2017
The majority of evolutionary processes, such as major phenotypic changes and
the origin of new species, take place over time periods too long to study
experimentally. Consequently, comparative biology has long been an important
resource in the study of macroevolution: evolution over thousands of
generations to millions of years. Phylogenetic comparative methods is the
discipline in which a phylogeny is used - often jointly with phenotypic
observations for species - to study the process and pattern of evolutionary
change through time and among taxa. This workshop focuses on teaching the
theory, implementation, and use of phylogenetic comparative methods, with
particular attention to methods implemented for the R statistical computing
Computer programs and packages
We recommend installing the most recent available version of R (https://www.r-project.org). For Mac users, we also encourage the installation of Rstudio (https://www.rstudio.com/). If you have any prior experience working with R, we also recommend you pre-install the most recent version of a number of R phylogenetics packages and their dependencies. More information can be found here.
Tuesday 1 August 2017.
- Introduction of instructors & students.
- Introduction to the phylogenetic comparative method. [PDF]
- Exercise 1: Introduction the basics of the R statistical computing environment. [URL]
- Exercise 2: Introduction to reading, writing, manipulating, and visualizing phylogenies and comparative data in R. [URL]
- Brownian motion and phylogenetically independent contrasts. [PDF]
- Exercise 3: Phylogenetically independent contrasts in R. [URL]
- Challenge problem 1: Challenge problem on contrasts regression. [solution]
- Excerise 4: Phylogenetic generalized least squares regression. [URL]
- Challenge problem 2: PGLS. [solution]
Wednesday 2 August 2017.
- Other models of continuous character evolution on trees. [PDF]
- Exercise 5: Fitting models of continuous character evolution. [URL]
- Challenge problem 3: Continuous character models. [solution]
- Discrete character evolution on phylogenies. [PDF]
- Exercise 6: Fitting discrete character evolution models to phylogenetic data in R. [URL]
- Challenge problem 4: Discrete character evolution. [solution]
- Ancestral state reconstruction for discrete & continuous characters. [PDF]
- Exercise 7: Ancestral state reconstruction for continuous characters. [URL]
Thursday 3 August 2017.
- Exercise 8: Ancestral state reconstruction for discrete characters. [URL]
- Challenge problem 5: Stochastic mapping discrete characters on a phylogeny. [solution]
- Pagel’s model for correlated binary trait evolution. [PDF]
- Exercise 9: Pagel’s model for studying the evolutionary correlation of discrete characters. [URL]
- Challenge problem 6: Examining the limitations of Pagel’s (1994) method. [solution]
- Introduction to multi-rate and multi-regime models of continuous character evolution. [PDF]
- Exercise 10: Multi-rate, multi-regime, and multivariate models for continuous trait evolution. [URL]
- Using reconstructed phylogenies to study the dynamics of species diversification. [PDF]
- Exercies 11: Introduction to studying diversification on phylogenies: LTT plots & γ. [URL]
- Measuring speciation and extinction rates on trees. [PDF]
Friday 4 August 2017.
- Exercise 12: Estimating speciation & extinction rates on phylogenies in R using ape and diversitree. [URL]
- Challenge problem 7: Exploring diversification models in R. [solution]
- Complex models of diversification. [PDF]
- Exercise 13: Fitting complex diversification models in R. [URL]
- State dependent diversification models. [PDF]
- Exercise 14: Fitting state-dependent diversification models in R using diversitree. [URL]
- SSB membership survey. [URL]
- Course evaluation survey. [URL]
- Exercise 15: Visualizing phylogenies and comparative data in R. [URL]
- Wrap-up and optional additional exercise or lecture.
Course co-taught by Ricardo Betancur, Luke Harmon, & Liam Revell. 1 August - 4 August 2017.
Co-organized by Santiago Benitez-Vieyra (Universidad Nacional de Córdoba - CONICET) and Marina Strelin (Universidad Nacional del Comahue), and funded by the National Science Foundation.