The importance of palaeontological data in divergence time estimation has increased with the introduction of Bayesian total-evidence dating methods, which use fossil taxa directly for calibration, facilitated by the joint analysis of morphological and molecular data. Fossil taxa are invariably incompletely known as a consequence of taphonomic processes, resulting in the decidedly non-random distribution of missing data. The impact of non-random missing data on the accuracy and precision of clade age estimation is unknown. In an attempt to constrain the impact of taphonomy on tip-calibrated dating analyses, we compared clade ages estimated from a very complete morphological matrix to ages estimated from the same matrix permuted to simulate the progressive loss of anatomical information resulting from taphonomic processes. We demonstrate that systematically distributed missing data negatively influence clade age estimates, but that successive stages within the taphonomic process introduce greater differences in age estimates, when compared to estimates obtained from untreated data. Despite these effects, the general influence of missing data is weak, presumably due to the compensatory effect of extensive morphological data from extant taxa. We suggest that, in the absence of models that can explicitly account for taphonomic processes, morphological datasets should be constructed to minimize the impact of taphonomy on divergence time estimation.

中文翻译：

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化石化过程对尖端校准的发散时间分析几乎没有影响

随着贝叶斯全证据定年方法的引入，古生物学数据在分歧时间估计中的重要性增加了，该方法直接使用化石分类群进行校准，并通过形态学和分子数据的联合分析来促进。由于埋藏过程，化石类群总是不完全已知，导致缺失数据的明显非随机分布。非随机缺失数据对进化枝年龄估计的准确性和精度的影响是未知的。为了限制埋藏学对尖端校准测年分析的影响，我们将从非常完整的形态矩阵估计的进化枝年龄与从相同矩阵估计的年龄进行比较，以模拟由埋藏过程导致的解剖信息的逐渐丢失。我们证明系统分布的缺失数据会对进化枝年龄估计产生负面影响，但与从未处理过的数据中获得的估计值相比，埋藏过程中的连续阶段引入了更大的年龄估计差异。尽管有这些影响，缺失数据的一般影响很弱，大概是由于来自现存分类群的大量形态学数据的补偿作用。我们建议，在缺乏可以明确解释埋藏过程的模型的情况下，应构建形态学数据集以最小化埋藏学对发散时间估计的影响。与从未经处理的数据中获得的估计值相比。尽管有这些影响，缺失数据的一般影响很弱，大概是由于来自现存分类群的大量形态学数据的补偿作用。我们建议，在缺乏可以明确解释埋藏过程的模型的情况下，应构建形态学数据集以最小化埋藏学对发散时间估计的影响。与从未经处理的数据中获得的估计值相比。尽管有这些影响，缺失数据的一般影响很弱，大概是由于来自现存分类群的大量形态学数据的补偿作用。我们建议，在缺乏可以明确解释埋藏过程的模型的情况下，应构建形态学数据集以最小化埋藏学对发散时间估计的影响。