Felsenstein, Joseph. 2004. Inferring Phylogenies. Vol. 2. Sinauer Associates Sunderland.

———. 2012. “A Comparative Method for Both Discrete and Continuous Characters Using the Threshold Model.” The American Naturalist 179 (2): 145–56.

Huelsenbeck, John P., Rasmus Nielsen, Jonathan P. Bollback, and Ted Schultz. 2003. “Stochastic Mapping of Morphological Characters.” Systematic Biology 52 (2): 131–58.

O’Meara, B. C. 2012. Evolutionary Inferences from Phylogenies: A Review of Methods. Vol. 43.

R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.

Steel, Mike, and David Penny. 2000. “Parsimony, Likelihood, and the Role of Models in Molecular Phylogenetics.” Molecular Biology and Evolution 17 (6): 839–50.

  1. Conditional probability is the probability of an event given that some other event has occurred. For example, you could use past information from your department to estimate Prob(getting tenure), but it is different if you use information that another event has occurred: Prob(getting tenure | made major discovery in evolution) is different from Prob(getting tenure | four years since last publication). In this domain (diversification alone and diversification plus trait models), we condition on actually having a tree to look at: if the true model is speciation rate equals extinction rate, there’s a good chance that most clades starting X million years ago will have gone extinct, so the ones we see diversified unusually quickly, and this has to be taken into account. The example I usually use for this is the idea that dolphins rescue drowning sailors. It’s known dolphins push interesting objects in the ocean. We could interview previously drowning sailors that dolphins pushed towards shore, and they’ll all say that dolphins saved them, but it’s very hard to interview sailors the dolphins pushed the other way. As always, Randall Munroe’s XKCD explains conditional probability best Seashell XKCD↩︎