Uncertainty in Divergence Time Estimation

Phylogenomic analyses of echinoid diversification prompt a re-evaluation of their fossil record

Echinoids are key components of modern marine ecosystems. Despite a remarkable fossil record, the emergence of their crown group is documented by few specimens of unclear affinities, rendering their early history uncertain. The origin of sand dollars, one of its most distinctive clades, is also unclear due to an unstable phylogenetic context. We employ 18 novel genomes and transcriptomes to build a phylogenomic dataset with a near-complete sampling of major lineages. With it, we revise the phylogeny and divergence times of echinoids, and place their history within the broader context of echinoderm evolution. We also introduce the concept of a chronospace - a multidimensional representation of node ages - and use it to explore methodological decisions involved in time calibrating phylogenies. We find the choice of clock model to have the strongest impact on divergence times, while the use of site-heterogeneous models and alternative node prior distributions show minimal effects. The choice of loci has an intermediate impact, affecting mostly deep Paleozoic nodes, for which clock-like genes recover dates more congruent with fossil evidence. Our results reveal that crown group echinoids originated in the Permian and diversified rapidly in the Triassic, despite the relative lack of fossil evidence for this early diversification. We also clarify the relationships between sand dollars and their close relatives and confidently date their origins to the Cretaceous, implying ghost ranges spanning approximately 50 million years, a remarkable discrepancy with their rich fossil record.

exTREEmaTIME: a method for incorporating uncertainty into divergence time estimates

We present a method of divergence time estimation (exTREEmaTIME) that aims to effectively account for uncertainty in divergence time estimates. The method requires a minimal set of assumptions, and, based on these assumptions, estimates the oldest possible divergence times and youngest possible divergence times that are consistent with the assumptions. We use a series of simulations and empirical analyses to illustrate that exTREEmaTIME is effective at representing uncertainty. We then describe how exTREEmaTIME can act as a basis to determine the implications of the more stringent assumptions that are incorporated into other methods of divergence time estimation that produce more precise estimates. This is critically important given that many of the assumptions that are incorporated into these methods are highly complex, difficult to justify biologically, and as such can lead to estimates that are highly inaccurate. This article has an associated First Person interview with the first author of the paper.