Data from: Assessing Progress in Systematics with Continuous Jackknife Function Analysis

Systematists expect their hypotheses to be asymptotically precise. As the number of phylogenetically informative characters for a set of taxa increases, the relationships implied should stabilize on some topology. If true, this increasing stability should clearly manifest itself if an index of congruence is plotted against the accumulating number of characters. Continuous Jackknife Function (CJF) analysis is a new graphical method that portrays the extent to which available data converge on a specified phylogenetic hypothesis, the reference tree. The method removes characters with increasing probability, analyzes the rarefied data matrices phylogenetically, and scores the clades shared between each of the resulting trees and the reference tree. As more characters are removed, the number of shared clades must decrease, but the rate of decrease will depend on how decisively the data support the reference tree. Curves for stable phylogenies are clearly asymptotic with nearly 100% congruence for a substantial part of the curve. Less stable phylogenies lose congruent nodes quickly as characters are excluded, resulting in a more linear or even a sigmoidal relationship. Curves can be interpreted as predictors of whether the addition of new data of the same type is likely to alter the hypothesis under test. Continuous Jackknife Function analysis makes statistical assumptions about the collection of character data. To the extent that CJF curves are sensitive to violations of unbiased character collection, they will be misleading as predictors. Convergence of data on a reference tree does not guarantee historical accuracy, but it does predict that the accumulation of further data under the sampling model will not lead to rapid changes in the hypothesis.系统发育学家期望其假说能够渐近精准。当一组类群的系统发育信息性状数量增加时，其所暗示的类群演化关系应收敛于某一拓扑结构。若此论断成立，则当将一致性指数随累积性状数量的变化绘制成图时，这种不断增强的稳定性应当清晰显现。连续刀切函数（Continuous Jackknife Function, CJF）分析法是一种新颖的图形化方法，用于刻画现有数据收敛于指定系统发育假说（即参考树）的程度。该方法以递增概率移除性状，对稀疏化后的数据集开展系统发育分析，并量化各所得支系树与参考树共有的演化支数量。随着移除的性状增多，共有演化支的数量必然减少，但其减少速率将取决于数据对参考树的支持强度。稳定系统发育的曲线呈现典型渐近特征，在曲线的大部分区间内一致性可达近100%；而稳定性较弱的系统发育则会在排除性状时快速丢失一致性节点，最终呈现出更接近线性甚至S形的关系。此类曲线可用于预测：添加同类新数据后，是否会快速改变当前待检验的假说。连续刀切函数分析法对性状数据的采集过程带有统计假设。若CJF曲线对无偏性状采集的偏差较为敏感，则其作为预测工具将产生误导性结果。数据收敛于参考树并不代表其具备历史真实性，但确实可以预测：在当前采样模型下继续积累数据，不会导致假说发生快速变动。