Data from: Implied weighting and its utility in palaeontological datasets: a study using modelled phylogenetic matrices

Implied weighting, a method for phylogenetic inference that actively seeks to downweight supposed homoplasy, has in recent years begun to be widely utilized in palaeontological datasets. Given the method's purported ability at handling widespread homoplasy/convergence, we investigate the effects of implied weighting on modelled phylogenetic data. We generated 100 character matrices consisting of 55 characters each using a Markov Chain morphology model of evolution based on a known phylogenetic tree. Rates of character evolution in these datasets were variable and generated by pulling from a gamma distribution for each character in the matrix. These matrices were then analysed under equal weighting and four settings of implied weights (k = 1, 3, 5, and 10). Our results show that implied weighting is inconsistent in its ability to retrieve a known phylogenetic tree. Equally weighted analyses are found to generally be more conservative, retrieving higher frequency of polytomies but being less likely to generate erroneous topologies. Implied weighting is found to generally resolve polytomies while also propagating errors, resulting in an increase in both correctly and incorrectly resolved nodes with a tendency towards higher rates of error compared to equal weighting. Our results suggest that equal weights may be a preferable method for parsimony analysis.

隐含加权（Implied weighting）是一种旨在主动压低推定同塑现象（homoplasy）权重的系统发育推断（phylogenetic inference）方法，近年来已在古生物学数据集（palaeontological datasets）中得到广泛应用。鉴于该方法据称具备处理广泛存在的同塑现象与趋同演化（convergence）的能力，本研究探讨了隐含加权法对模拟系统发育数据的影响。本研究基于已知系统发育树，采用马尔可夫链形态演化模型（Markov Chain morphology model of evolution）生成了100组性状矩阵（character matrices），每组矩阵包含55个性状。本数据集内的性状演化速率呈异质性，通过对矩阵内每个性状采样自伽马分布（gamma distribution）生成。随后分别采用等权分析（equal weighting）与四种隐含加权参数设置（k=1、3、5、10）对这些矩阵进行分析。本研究结果显示，隐含加权法在恢复已知系统发育树的能力上表现不一致。研究发现，等权分析通常更为保守，虽能以更高频率生成多歧分支（polytomies），但更不易产生错误的拓扑结构（topologies）。而隐含加权法则通常可解决多歧分支问题，但同时也会放大误差：与等权分析相比，其正确与错误解析的节点数量均有所增加，且错误率整体更高。本研究结果表明，在简约法分析（parsimony analysis）中，等权法或许是更为优选的方法。