Stochastic Character Mapping of Continuous Traits on Phlyogenies

Abstract

AO_SCPLOWBSTRACTC_SCPLOWFossilized organisms only represent a small fraction of Earths evolutionary history, motivating "ancestral state reconstruction" techniques for inferring unobserved phenotypes of evolving lineages based on measurements of their relatives. Stochastic character mapping has emerged as a particularly powerful approach in this regard, allowing researchers to sample histories of discrete variables on phylogenies and better account for the inherent uncertainty of reconstructed ancestral states. Here, we generalize this procedure to work with continuous variables by developing an efficient algorithm for sampling evolutionary histories under Brownian Motion, implemented in a new R package contsimmap. To demonstrate potential applications of these "continuous stochastic character maps", we develop a pipeline for inferring relationships between rates of continuous trait evolution and continuously-varying factors (e.g., body size, generation time)--a difficult statistical problem for which relatively few methods are available. After verifying this novel pipelines performance on simulated data, we use it to show that smaller eucalypt trees tend to exhibit higher rates of flower and leaf trait evolution overall, aligning with well-established predictions based on life history theory as well as empirical patterns in other systems. Ultimately, continuous stochastic character mapping is a valuable new tool for analyzing macroevolutionary data, enabling rigorous yet flexible investigation of complex evolutionary dynamics involving continuous traits and/or continuous variables hypothesized to affect evolutionary processes.