Data from: Time for a rethink: time sub-sampling methods in disparity-through-time analyses

Disparity-through-time analyses can be used to determine how morphological diversity changes in response to mass extinctions, and to investigate the drivers of morphological change. These analyses are routinely applied to palaeobiological datasets, yet although there is much discussion about how to best calculate disparity, there has been little consideration of how taxa should be sub-sampled through time. Standard practice is to group taxa into discrete time bins, often based on stratigraphic periods. However, this can introduce biases when bins are of unequal size, and implicitly assumes a punctuated model of evolution. In addition, many time bins may have few or no taxa, meaning that disparity cannot be calculated for the bin and making it harder to complete downstream analyses. Here we describe a different method to complement the disparity-through-time tool-kit: time-slicing. This method uses a time-calibrated phylogenetic tree to sample disparity-through-time at any fixed point in time rather than binning taxa. It uses all available data (tips, nodes and branches) to increase the power of the analyses, specifies the implied model of evolution (punctuated or gradual), and is implemented in R. We test the time-slicing method on four example datasets and compare its performance in common disparity-through-time analyses. We find that the way you time sub-sample your taxa can change your interpretations of the results of disparity-through-time analyses. We advise using multiple methods for time sub-sampling taxa, rather than just time binning, to gain a better understanding disparity-through-time.形态差异随时间变化分析（disparity-through-time）可用于探究集群灭绝事件对形态多样性的影响，以及形态演化的驱动因素。这类分析常规应用于古生物学数据集（palaeobiological datasets），尽管学界围绕最优形态差异计算方式已有大量讨论，但极少有人关注分类群（taxa）应如何随时间进行二次抽样。 常规做法是将分类群按离散时间区间分组，通常基于地层年代（stratigraphic periods）划分。但当区间大小不均时，该做法会引入偏差，且隐含假定了间断平衡演化模型。此外，诸多时间区间内可能几乎没有或完全没有分类群，导致无法计算该区间的形态差异，进而增加下游分析的难度。 本文介绍一种可补充形态差异随时间变化分析工具集的新方法：时间切片法（time-slicing）。该方法借助时间校准系统发育树，在任意固定时间点采样形态差异随时间的变化，而非对分类群进行区间分组。其利用所有可用数据（枝端、节点与分支）提升分析效力，明确指定其所隐含的演化模型（间断平衡型或渐变型），且已在R语言中实现。 我们在四组示例数据集上对时间切片法进行测试，并对比其在常规形态差异随时间变化分析中的表现。研究发现，对分类群进行时间二次抽样的方式会改变研究者对形态差异随时间变化分析结果的解读。我们建议采用多种分类群时间二次抽样方法，而非仅依赖时间区间分组，以更全面地认识形态差异随时间变化的规律。