Abstract
The development of methods to estimate rates of speciation and extinction from time-calibrated phylogenies has revolutionized evolutionary biology by allowing researchers to correlate diversification rate shifts with causal factors. A growing number of researchers are interested in testing whether the evolution of a trait or a trait variant has influenced speciation rate, and three modelling methods—BiSSE, MEDUSA and BAMM—have been widely used in such studies. We simulated phylogenies with a single speciation rate shift each, and evaluated the power of the three methods to detect these shifts. We varied the degree of increase in speciation rate (speciation rate asymmetry), the number of tips, the tip-ratio bias (ratio of number of tips with each character state) and the relative age in relation to overall tree age when the rate shift occurred. All methods had good power to detect rate shifts when the rate asymmetry was strong and the sizes of the two lineages with the distinct speciation rates were large. Even when lineage size was small, power was good when rate asymmetry was high. In our simulated scenarios, small lineage sizes appear to affect BAMM most strongly. Tip-ratio influenced the accuracy of speciation rate estimation but did not have a strong effect on power to detect rate shifts. Based on our results, we provide suggestions to users of these methods.
Highlights
Much as the advent of methods to infer phylogenies (Edwards & Cavalli-Sforza, 1963; Sokal & Sneath, 1963; Camin & Sokal, 1965; Hennig, 1965) led to a spectacular revolution in evolutionary biology, the arrival of mathematical methods to estimate divergence times from molecular phylogenies has offered unprecedented novel insights into macroevolution
In analyses of trees from Simulation Set 1, power tended to increase both with asymmetry and tree size across all three subtree ages (Fig. 2), but the overall effect of asymmetry and tree size depended on subtree age (Sage)
We find that power to detect speciation rate shifts is strongly influenced by rate asymmetry and tip number for all methods
Summary
Much as the advent of methods to infer phylogenies (Edwards & Cavalli-Sforza, 1963; Sokal & Sneath, 1963; Camin & Sokal, 1965; Hennig, 1965) led to a spectacular revolution in evolutionary biology, the arrival of mathematical methods to estimate divergence times from molecular phylogenies has offered unprecedented novel insights into macroevolution. A seminal innovation has been the development of tools to estimate rates of speciation and extinction from time-calibrated phylogenies (Nee, May & Harvey, 1994). Until such methods became available, our understanding of macroevolutionary patterns and processes of diversification largely relied on the fossil record, which is incomplete for most taxa (Benton, Wills & Hitchin, 2000; Quental & Marshall, 2010) and virtually non-existent for many soft-bodied life forms (Donoghue & Purnell, 2009). One of the most common themes in macroevolutionary studies over the last decade has been to test whether a trait (or trait variant) has increased speciation rates (e.g., Sundue Michael, Testo Weston & Ranker Tom, 2015; Claramunt et al, 2012; Escudero et al, 2012; Litsios et al, 2012; Horn James et al, 2014; Rainford et al, 2014; Xiang et al, 2014; Gubry-Rangin et al, 2015; Igea et al, 2017; Wiens, 2017; Sahoo et al, 2017; Seeholzer, Claramunt & Brumfield, 2017), with a suite of analytical tools providing the framework to infer speciation rate variation across the phylogeny
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