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.哺乳动物的高度演化成功与诸多行为及生活史特征密切相关，其中多数特征又与脑容量息息相关。然而，针对这一关键性状演化的研究尚未充分挖掘化石记录的全部潜力，其瓶颈在于难以从化石中获取颅腔容积（endocranial volume）相关数据。本研究针对199个现生哺乳动物物种的503件成年标本展开测量，涵盖了133个现生哺乳动物科中的99个科，记录了脑颅的颅腔容积、长度、高度及宽度等参数。我们在多元回归法的基础上进行拓展，构建了一套通过颅骨外部测量数据估算脑容量的公式。此外，我们还对非哺乳类合弓动物（synapsids）进行了分析，以评估本模型应用的系统发育边界。模型预测的颅腔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。若针对采样充分的亚支系（如灵长类或啮齿类）构建专属模型，预测误差会进一步降低，这表明脑容量与颅骨大小之间的关系存在一定的演化分化。不过，采用系统发育独立对比（phylogenetically independent contrasts）法进行的重新分析显示，其系统发育依赖性较弱，说明本模型可适用于系统发育亲缘关系未知的物种的颅腔容积估算。因此，本模型具备普适性强、快速高效且成本低廉的优势，能够极大拓展哺乳动物脑容量数据集的分类学与时间跨度。即便对于鲸类、单孔目等颅骨高度特化的类群，其颅腔容积也可通过本模型进行可靠估算。不过，该模型在广义胎盘类颅骨上的表现最佳。颅骨结构的根本性差异表明，本模型无法对比摩尔根兽（Morganucodon，约2亿年前）更基干的非哺乳类合弓动物的颅腔容积做出精准估算。因此，本模型不适用于系统发育距离哺乳冠群（mammalian crown group）较远的类群，但可用于估算亲缘关系较近的类群之间的相对脑容量。