Bristol Palaeobiology Group, School of Earth Sciences, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, UK.
Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, UK.
Proc Biol Sci. 2023 Aug 9;290(2004):20230522. doi: 10.1098/rspb.2023.0522.
Analyses of morphological disparity can incorporate living and fossil taxa to facilitate the exploration of how phenotypic variation changes through time. However, taphonomic processes introduce non-random patterns of data loss in fossil data and their impact on perceptions of disparity is unclear. To address this, we characterize how measures of disparity change when simulated and empirical data are degraded through random and structured data loss. We demonstrate that both types of data loss can distort the disparity of clades, and that the magnitude and direction of these changes varies between the most commonly employed distance metrics and disparity indices. The inclusion of extant taxa and exceptionally preserved fossils mitigates these distortions and clarifies the full extent of the data lost, most of which would otherwise go uncharacterized. This facilitates the use of ancestral state estimation and evolutionary simulations to further control for the effects of data loss. Where the addition of such reference taxa is not possible, we urge caution in the extrapolation of general patterns in disparity from datasets that characterize subsets of phenotype, which may represent no more than the traits that they sample.
形态差异分析可以纳入活体和化石分类单元,以促进探索表型变异随时间如何变化。然而,埋藏学过程会导致化石数据中出现非随机的数据缺失模式,其对差异感知的影响尚不清楚。为了解决这个问题,我们描述了在通过随机和结构化数据缺失模拟和经验数据时,差异度量如何变化。我们证明,这两种类型的数据缺失都会扭曲类群的差异,而且这些变化的幅度和方向在最常用的距离度量和差异指数之间有所不同。包括现存分类单元和保存异常完好的化石可以减轻这些扭曲,并阐明丢失数据的全部范围,否则其中大部分数据将无法描述。这有助于使用祖先状态估计和进化模拟进一步控制数据缺失的影响。如果无法添加此类参考分类单元,我们强烈建议在从仅反映部分表型特征的数据集推断差异的一般模式时保持谨慎,因为这些数据集可能只代表它们所采样的特征。