Phylogenies and diversification rates: variance cannot be ignored

Abstract

The concept of variance is the foundation of modern statistics; it reflects our awareness that independent samples from a single population or stochastic process can produce a range of outcomes. A recent pair of articles in the journal Evolution abandons the notion of the sample variance and advocates for uncorrected comparisons of numerical point estimates between groups. The articles in question (Meyer and Wiens 2017; Meyer et al., 2018) criticize BAMM, a scientific software program that uses a Bayesian mixture model to estimate rates of evolution from phylogenetic trees. The authors use BAMM to estimate rates from large phylogenies (n > 60 tips) and they apply the method separately to subclades within those phylogenies (median size: n = 3 tips); they find that point estimates of rates differ between these levels and conclude that the method is flawed, but they do not test whether the observed differences are statistically meaningful. There is no consideration of sampling variation and its impact at any level of their analysis. Here, I show that numerical differences across groups that they report are fully explained by high variance in their subclade estimates, which is approximately 55 times greater than the corresponding variance for estimates from large phylogenies. Variance in evolutionary rate estimates - from BAMM and all other methods - is an inverse function of clade size; this variance is extreme for clades with 5 or fewer tips (e.g., 70% of clades in the focal study). The articles in question rely on negative results that are easily explained by low statistical power to reject their preferred null hypothesis, and this low power is a trivial consequence of high variance in their point estimates. By ignoring variance, the testing approach outlined in these articles can be misused to demonstrate that all statistical estimators, including the arithmetic mean, are \"flawed\". I describe additional mathematical and statistical mistakes that render the proposed testing framework invalid on first principles. Evolutionary rates are no different than any other population parameters we might wish to estimate, and biologists should use the training and tools already at their disposal to avoid erroneous results that follow from the neglect of variance.