Data from: The impact of rate heterogeneity on inference of phylogenetic models of trait evolution

Rates of trait evolution are known to vary across phylogenies; however, standard evolutionary models assume a homogeneous process of trait change. These simple methods are widely applied in small-scale phylogenetic studies, whereas models of rate heterogeneity are not, so the prevalence and patterns of potential rate variation in groups up to hundreds of species remain unclear. The extent to which trait evolution is modelled accurately on a given phylogeny is also largely unknown because studies typically lack absolute model fit tests. We investigated these issues by applying both rate-static and variable-rates methods on (i) body mass data for 88 avian clades of 10–318 species, and (ii) data simulated under a range of rate-heterogeneity scenarios. Our results show that rate heterogeneity is present across small-scaled avian clades, and consequently applying only standard single-process models prompts inaccurate inferences about the generating evolutionary process. Specifically, these approaches underestimate rate variation, and systematically mislabel temporal trends in trait evolution. Conversely, variable-rates approaches have superior relative fit (they are the best model) and absolute fit (they describe the data well). We show that rate changes such as single internal branch variations, rate decreases and early bursts are hard to detect, even by variable-rates models. We also use recently developed absolute adequacy tests to highlight misleading conclusions based on relative fit alone (e.g. a consistent preference for constrained evolution when isolated terminal branch rate increases are present). This work highlights the potential for robust inferences about trait evolution when fitting flexible models in conjunction with tests for absolute model fit.

众所周知，性状演化速率在不同系统发育树（phylogeny）间存在差异；然而标准演化模型均假定性状变化遵循均一过程。这类简易方法被广泛应用于小规模系统发育研究，但速率异质性（rate heterogeneity）模型的推广程度却相对有限，因此针对包含数百个物种的类群，其潜在速率变异的普遍性与模式仍不明确。此外，在特定系统发育树下，性状演化模型的拟合准确程度在很大程度上仍不清楚，因多数研究未开展绝对模型拟合检验（absolute model fit tests）。针对上述问题，本研究采用速率静态法与可变速率法开展两组分析：其一为涵盖10至318个物种的88个鸟类支系的体重数据分析；其二为一系列速率异质性场景下模拟生成的数据的分析。研究结果显示，小型鸟类支系中普遍存在速率异质性，因此仅使用标准单过程模型会导致对真实演化过程的推断出现偏差。具体而言，这类方法会低估速率变异程度，并系统性地错误推断性状演化的时间趋势。与之相对，可变速率法具备更优的相对拟合效果（为最优模型）与绝对拟合效果（可较好地拟合数据）。本研究还发现，即便采用可变速率模型，也难以检测到诸如单个内部枝变异、速率下降以及早期爆发等类型的速率变化。此外，本研究借助新近提出的绝对充分性检验（absolute adequacy tests），揭示了仅基于相对拟合度得出的误导性结论——例如，当存在孤立的末端枝速率升高时，模型会持续倾向于推断演化过程受约束。本研究表明，若结合使用灵活模型与绝对模型拟合检验，即可实现对性状演化的稳健推断。