Rates of phenotypic evolution vary markedly across the tree of life, from the accelerated evolution apparent in adaptive radiations to the remarkable evolutionary stasis exhibited by so-called "living fossils". Such rate variation has important consequences for large-scale evolutionary dynamics, generating vast disparities in phenotypic diversity across space, time, and taxa. Despite this, most methods for estimating trait evolution rates assume rates vary deterministically with respect to some variable of interest or change infrequently during a clade's history. These assumptions may cause underfitting of trait evolution models and mislead hypothesis testing. Here, we develop a new trait evolution model that allows rates to vary gradually and stochastically across a clade. Further, we extend this model to accommodate generally decreasing or increasing rates over time, allowing for flexible modeling of "early/late bursts" of trait evolution. We implement a Bayesian method, termed "evolving rate" (evorates for short), to efficiently fit this model to comparative data. Through simulation, we demonstrate that evorates can reliably infer both how and in which lineages trait evolution rates varied during a clade's history. We apply this method to body size evolution in cetaceans, recovering substantial support for an overall slowdown in body size evolution over time with recent bursts among some oceanic dolphins and relative stasis among beaked whales of the genus Mesoplodon. These results unify and expand on previous research, demonstrating the empirical utility of evorates.
Phylogenies form the backbone of many modern comparative methods and are integral components of contemporary science communication. Recent years have seen drastic increases in both the size and complexity of phylogenetic data as computational resources and genetic/trait databases expand. Graphical representations of these massive phylogenetic datasets push against the limits of legibility, often veering closer to artwork than scientific figures optimized to communicate results. While attractive scientific illustrations are certainly a laudable goal, researchers may want to opt for simpler representations to communicate results more concisely. Here, we introduce a new R package, shiftPlot, which implements methods for simplifying and plotting phylogenetic comparative data on discrete traits. Specifically, shiftPlot automatically finds and collapses clades exhibiting the same character state, effectively creating smaller phylogenies that may be more legibly rendered on standard page sizes. Further, these visualizations more clearly communicate evolutionary dynamics by emphasizing state shifts over tip states. While there are undoubtedly situations where this graphical approach will not be suitable (e.g., continuous traits), we believe shiftPlot will prove useful for modern researchers faced with the task of communicating the results of complex phylogenetic analyses.
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