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.

时间跨度形态差异分析（disparity-through-time）可用于探究形态多样性如何响应集群灭绝而发生变化，同时亦可揭示形态演化变化的驱动因素。这类分析常被应用于古生物数据集（palaeobiological datasets）中，尽管学界已围绕如何最优计算形态差异展开了诸多讨论，但却极少关注分类单元（taxa）应如何随时间进行子采样。当前的标准流程是将分类单元按离散的时间间隔箱（time bins）进行分组，分组依据通常为地层年代。然而，当各时间间隔箱的规模不均时，该方法会引入偏倚，且其隐含地假设了间断平衡演化模型（punctuated model of evolution）。此外，众多时间间隔箱可能仅包含少量分类单元甚至无分类单元，导致无法计算该箱内的形态差异，进而增加了下游分析的完成难度。本文介绍一种可补充时间跨度形态差异分析工具集（disparity-through-time tool-kit）的全新方法：时间切片法（time-slicing）。该方法借助时间校准系统发育树（time-calibrated phylogenetic tree），可在任意固定时间点上采样形态差异随时间的变化，而非对分类单元进行分箱处理。该方法利用所有可用数据（叶节点、内部节点及进化分支）以提升分析效力，明确了其隐含的演化模型（间断平衡或渐变式演化），且已在R语言中完成实现。我们基于四组示例数据集对时间切片法进行了测试，并将其与常见的时间跨度形态差异分析方法的性能进行了对比。研究结果表明，对分类单元进行时间子采样的方式，会改变研究者对时间跨度形态差异分析结果的解读。我们建议，为更全面地理解形态差异随时间的变化规律，应采用多种分类单元时间子采样方法，而非仅依赖时间间隔箱分组法。