Ancestral State Estimation with Phylogenetic Ridge Regression

Silvia Castiglione, Carmela Serio, Alessandro Mondanaro, Marina Melchionna, Francesco Carotenuto, Mirko Di Febbraro, Antonio Profico, Davide Tamagnini, Pasquale Raia*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The inclusion of fossil phenotypes as ancestral character values at nodes in phylogenetic trees is known to increase both the power and reliability of phylogenetic comparative methods (PCMs) applications. We implemented the R function RRphylo as to integrate fossil phenotypic information as ancestral character values. We tested the new implementation, named RRphylo-noder (which is available as part of the RRphylo R package) on tree and data generated according to evolutionary processes of differing complexity and under variable sampling conditions. We compared RRphylo-noder performance to other available methods for ancestral state estimation, including Bayesian approaches and methods allowing rate variation between the tree branches. We additionally applied RRphylo-noder to two real cases studies, the evolution of body size in baleen whales and in caniform carnivores. Variable-rate methods proved to be more accurate than single-rate methods in estimating ancestral states when the pattern of phenotypic evolution changes across the tree. RRphylo-noder proved to be slightly more accurate and sensibly faster than Bayesian approaches, and the least sensitive to the kind of phenotypic pattern simulated. The use of fossil phenotypes as ancestral character values noticeably increases the probability to find a phenotypic trend through time when it applies to either the entire tree or just to specific clades within it. We found Cope’s rule to apply to both mysticete cetaceans and caniform carnivores. The RRphylo-noder implementation is particularly appropriate to study phenotypic evolution in the presence of complex phenotypes generated by different processes acting in different parts the tree, and when suitable information about fossil phenotypes is at hand.

Original languageEnglish
Pages (from-to)220-232
Number of pages13
JournalEvolutionary Biology
Early online date17 Jun 2020
Publication statusPublished - 1 Sept 2020


  • Ancestral states estimation
  • Fossil phylogenies
  • Phenotypic evolution
  • Phylogenetic comparative methods
  • Rrphylo

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