Data from: Multiple regression modelling for estimating endocranial volume in extinct Mammalia

The profound evolutionary success of mammals has been linked to behavioral and life-history traits, many of which have been tied to brain size. However, studies of the evolution of this key trait have yet to explore the full potential of the fossil record, being limited by the difficulty of obtaining endocranial data from fossils. Using measurements of endocranial volume, length, height, and width of the braincase in 503 adult specimens from 199 extant species, representing 99 of 133 extant mammalian families, we expand upon a simple method of using multiple regression to develop a formula for estimating brain size from external skull measurements. We also examined non- mammalian synapsids to assess the phylogenetic limits of our model's application. Model-predicted volume correlates strongly with measured volume (R2 = 0.993) and prediction error is between 16% and 19%. Error decreases if models developed for well-sampled subclades such as primates or rodents are used, demonstrating that some differential evolution of the relationship between brain size and skull size has occurred. However, reanalysis using phylogenetically independent contrasts demonstrates weak phylogenetic dependency, indicating that our model is appropriate for estimating the endocranial volume of species of unknown phylogenetic affinity. Thus, the model represents a generally applicable, fast and cost-efficient way to dramatically expand the taxonomic and temporal scope of mammalian brain size data sets. Even endocranial volumes of taxa with highly derived crania, such as cetaceans and monotremes, can be estimated confidently. However, the model works best for generalized placental crania. Fundamental differences in cranial architecture suggest that the model cannot provide accurate estimates of endocranial volume in non-mammalian synapsids more basal than Morganucodon (ca. 200 Ma). Therefore, use of the model for taxa phylogenetically distant from the mammalian crown group is not warranted, but it might be used to establish relative brain sizes between closely related subgroups.

哺乳动物在演化上的卓越成功，与其行为学特征及生活史策略密切相关，而其中诸多特征又与脑容量存在显著关联。然而，针对脑容量这一关键演化特征的研究，尚未充分发掘化石记录的全部潜力——其核心瓶颈在于难以从化石标本中获取颅腔内（endocranial）相关数据。本研究基于覆盖133个现生哺乳动物科中99个科的199个物种的503件成年标本，测量其脑颅的容积、长度、高度及宽度，并在多元回归简易方法的基础上，开发出一套可通过颅骨外部测量值估算脑容量的公式。我们还对非哺乳类合弓纲（synapsids）开展了分析，以评估本模型的系统发育应用边界。模型预测的颅内容积与实测值相关性极强（决定系数R²=0.993），预测误差介于16%至19%之间。若使用针对采样充分的演化亚支（如灵长类或啮齿类）开发的子模型，预测误差会进一步降低，这表明脑容量与颅骨尺寸之间的关联模式存在一定的分化演化。不过，通过系统发育独立对比（phylogenetically independent contrasts）开展的重新分析显示，模型预测结果仅存在微弱的系统发育依赖性，这表明本模型可适用于未知系统发育亲缘关系物种的颅内容积估算。因此，本模型具备普适性强、快速高效且成本低廉的优势，可大幅拓展哺乳动物脑容量数据集的分类学与时间跨度。即便是鲸类、单孔目这类颅骨高度特化的类群，其颅内容积也可得到可靠估算。不过，本模型在具有典型形态的胎盘类颅骨上表现最优。颅骨结构的根本性差异表明，本模型无法为比摩尔根兽（Morganucodon，ca. 200 Ma）更为基部的非哺乳类合弓纲动物提供准确的颅内容积估算值。因此，本模型不适用于系统发育距离哺乳类冠群较远的类群，但可用于估算亲缘关系较近的亚群之间的相对脑容量。