phylogeny of caniform carnivoresA time-calibrated phylogeny of caniform carnivora used in the paper to estimate rates of body size evolution. The phylogeny is composed on individually inferred family level trees appended to a backbone from Eizirik et al. (2010: MPE). Full details of phylogeny reconstruction are provided in appendix 2 of the paper.caniform.phycaniform body mass dataNatural log transformed body mass data for Caniformia used to fit models of evolution.caniform_mass.csvcaniform body size priorsInformative prior distributions for nodes in the caniform phylogeny. Full details are provided in Appendix 2 of the paper.priors.csvcaniform analysisAn R script containing code to perform the analyses done with the caniform carnivore dataset in the paper.simulation phylogenyA simulated, non-ultrametric phylogenetic tree generated using the birthdeath.tree function in the geiger package. This tree was used for simulation tests documented in the paper.100taxon.phyfossils as tips additionR script for performing simulation tests where fossils are treated as tip taxa and models are fitted to data using Maximum likelihood. For space and clarity, simulations under different models have been added to a single script and commented out, such that individual models can be uncommented and run as required. It should also be noted that this script was written to be used on a cluster and thus to be distributed to many nodes at a single time. Users without access to a cluster may wish to loop this script multiple times if attempting to replicate results, but should take care not to overwrite previous results.fossils as tips swappingThis script is identical to the fossils as tips addition script, except that here proportions of extant taxa are replaced with fossil taxa to test the signal contributed by fossils relative to extant taxa. See the description for the aforementioned script for further details/ warnings.fossils as nodes actual valuesThis R script conducts MCMC analyses using informative node priors centered on the nodes' true values. The script was written to run multiple analyses simultaneously on a cluster. See fossils as tips description for more information.fossils as nodes fossil recordThis R script performs simulation tests using MCMC using a simulated fossil record to derive informative node priors for a comparative dataset. Results are those in Appendix 1.fitContinousMCMCAn R script containing source code for functions related to fitContinuousMCMC. These are beta versions of the code that were used to conduct analyses in the manuscript. More user-friendly code with expanded options (different priors, for example) is available in a temporary package from http://www.webpages.uidaho.edu/~lukeh/software/index.html or from Graham Slater at gslater@ucla.edu

Comparative biologists often attempt to draw inferences about tempo and mode in evolution by comparing the fit of evolutionary models to phylogenetic comparative data consisting of a molecular phylogeny with branch lengths and trait measurements from extant taxa. These kinds of approaches ignore historical evidence for evolutionary pattern and process contained in the fossil record. In this paper we show through simulation that incorporation of fossil information dramatically improves our ability to distinguish among models of quantitative trait evolution using comparative data. We further suggest a novel Bayesian approach that allows fossil information to be integrated even when explicit phylogenetic hypothesis are lacking for extinct representatives of extant clades. By applying this approach to a comparative dataset comprising body sizes for caniform carnivorans, we show that incorporation of fossil information not only improves ancestral state estimates relative to those derived from extant taxa alone, but also results in preference of a model of evolution with trend towards large body size over alternative models such as Brownian motion or Ornstein-Uhlenbeck processes. Our approach highlights the importance of considering fossil information when making macroevolutionary inference, and provided a way to integrate the kind of sparse fossil information that is available to most evolutionary biologists.