Online Course – 5th Edition
Mapping Trait Evolution
July 4th-8th, 2022
Evolution
This course will be delivered live online
Course overview
The course provides a comprehensive overview of the state-of-the-art methods in mapping phenotypic trait evolution and will provide participants with a springboard to using these methods to answering their own research questions.
We focus on analyses that use a phylogenetic tree and observed trait information from tip taxa (extant and/or extinct) to describe how traits have changed along the branches of a phylogeny. The course covers methods that estimate and test patterns related to changes in mean, covariation, and rate. Applications for continuous and categorical, and univariate and multivariate research designs are discussed.
At the end of this course, participants will have developed an understanding of:
(1) Brownian motion and Ornstein-Uhlenbeck models of evolution.
(2) How these models can be applied to estimate and test patterns of trait evolution.
(3) What the advantages and disadvantages are of different models/methods.
(4) How to recognize which model/method is most appropriate given a particular dataset and research question.
We provide several data sets that will be used to exemplify the application of these methods. We do, however, encourage participants to work with their own data so as to get direct experience with analysing precisely what they expect to analyse.
Methods from the following R packages will be discussed: ape, geiger, phytools, evomap, l1ou, bayou, surface, OUwie, mvMORPH, geomorph (this list may change as new packages become available).
Important note: Please bear in mind that this course is not about reconstructing (building) phylogenetic trees, the methods we cover in this course assume that a phylogeny is known.
Requirements
Graduate or postgraduate degree in any Biosciences discipline. Knowledge of multivariate statistics.
A reminder of R-skills that are of particular relevance when applying phylogenetic comparative methods will be given the first day, though a medium level of R knowledge is needed for following the course (i.e. knowledge on how to load and save data, manipulate data frames, use basic plotting functions, and use functions to analyse data).
Participants must have a personal computer (Windows, Mac, Linux). The use of a webcam and headphones is strongly recommended, and a good internet connection.
