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.

隐含加权法（Implied Weighting）是一种旨在下调疑似同塑演化（homoplasy）权重的系统发育推断方法，近年来已在古生物数据集中得到广泛应用。鉴于该方法据称可有效处理大范围同塑演化与趋同演化，本研究探讨了隐含加权法对模拟系统发育数据的影响。本研究基于一棵已知系统发育树，利用马尔可夫链形态演化模型生成了100组性状矩阵，每组矩阵包含55个性状。本数据集内的各性状演化速率存在差异，每组矩阵中的每个性状的演化速率均通过伽马分布（gamma distribution）抽样获得。随后，分别采用等权重法以及四种隐含加权参数设置（k=1、3、5、10）对这些性状矩阵进行分析。研究结果表明，隐含加权法在恢复已知系统发育树的能力上并不稳定。分析显示，等权重分析通常更为保守，虽能产生更高频率的多歧支（polytomies），但生成错误拓扑结构的概率更低。而隐含加权法通常可解决多歧支问题，但同时也会放大误差：相较于等权重法，其正确与错误解析的节点数量均有所增加，且错误率整体偏高。本研究结果表明，在简约分析（parsimony analysis）中，等权重法或许是更为优选的分析方法。