Data from: Using geometric morphometric visualizations of directional selection gradients to investigate morphological differentiation

Researchers studying extant and extinct taxa are often interested in identifying the evolutionary processes that have lead to the morphological differences among the taxa. Ideally, one could distinguish the influences of neutral evolutionary processes (genetic drift, mutation) from natural selection, and in situations for which selection is implicated, identify the targets of selection. The directional selection gradient is an effective tool for investigating evolutionary process, because it can relate form (size and shape) differences between taxa to the variation and covariation found within taxa. However, although most modern morphometric analyses use the tools of geometric morphometrics (GM) to analyze landmark data, to date, selection gradients have mainly been calculated from linear measurements. To address this methodological gap, here we present a GM approach for visualizing and comparing between-taxon selection gradients with each other, associated difference vectors, and "selection" gradients from neutral simulations. To exemplify our approach, we use a dataset of 347 three-dimensional landmarks and semilandmarks recorded on the crania of 260 primate specimens (112 humans, 67 common chimpanzees, 36 bonobos, 45 gorillas). Results on this example dataset show how incorporating geometric information can provide important insights into the evolution of the human braincase, and serve to demonstrate the utility of our approach for understanding morphological evolution.

研究现生与灭绝类群的学者往往致力于识别驱动类群间形态差异的演化过程。理想状态下，研究者可将中性演化过程（遗传漂变、突变）的影响与自然选择区分开；若确定存在选择作用，还可进一步明确选择的靶标。定向选择梯度是探究演化过程的有效工具，因其能够将类群间的形态（大小与形状）差异与类群内部的变异及协变异建立关联。尽管当前多数现代形态计量分析均采用几何形态计量学（geometric morphometrics, GM）方法处理地标数据，但迄今为止，选择梯度的计算仍主要基于线性测量数据。为填补这一方法学空白，本文提出一种基于几何形态计量学的分析框架，用于可视化并比较类群间的选择梯度、相关差异向量，以及中性模拟得到的“选择”梯度。为演示该方法的应用，本文使用了一套数据集：对260件灵长类标本（含112例人类、67例普通黑猩猩、36例倭黑猩猩、45例大猩猩）的颅骨采集了347个三维地标与半地标。基于该示例数据集的分析结果表明，纳入几何形态信息可为人类颅腔的演化研究提供重要洞见，同时验证了本方法在理解形态演化问题上的应用价值。