Gene tree discordance is expected in phylogenomic trees and biological processes are often invoked to explain it. However, heterogeneous levels of phylogenetic signal among individuals within datasets may cause artifactual sources of topological discordance. We examined how the information content in tips and subclades impacts topological discordance in the parrots (Order: Psittaciformes), a diverse and highly threatened clade of nearly 400 species. Using ultraconserved elements from 96% of the clade's species-level diversity, we estimated concatenated and species trees for 382 ingroup taxa. We found that discordance among tree topologies was most common at nodes dating between the late Miocene and Pliocene, and often at the taxonomic level of genus. Accordingly, we used two metrics to characterize information content in tips and assess the degree to which conflict between trees was being driven by lower quality samples. Most instances of topological conflict and non-monophyletic genera in the species tree could be objectively identified using these metrics. For subclades still discordant after tip-based filtering, we used a machine learning approach to determine whether phylogenetic signal or noise was the more important predictor of metrics supporting the alternative topologies. We found that when signal favored one of the topologies, noise was the most important variable in poorly performing models that favored the alternative topology. In sum, we show that artifactual sources of gene tree discordance, which are likely a common phenomenon in many datasets, can be distinguished from biological sources by quantifying the information content in each tip and modeling which factors support each topology.

中文翻译：

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世界鹦鹉的系统基因组学分析区分了基因树不一致的人为来源和生物来源

在系统发育树中预期基因树不一致，并且经常调用生物学过程来解释它。然而，数据集中个体之间系统发育信号的异质性水平可能会导致拓扑不一致的人为来源。我们研究了尖端和亚进化枝中的信息内容如何影响鹦鹉（目：鹦鹉形目）的拓扑不一致性，鹦鹉是近 400 个物种的多样化和高度受威胁的进化枝。使用来自 96% 的进化枝物种水平多样性的超保守元素，我们估计了 382 个内群分类群的级联树和物种树。我们发现树拓扑结构的不一致在中新世晚期和上新世之间的节点处最为常见，并且通常在属的分类水平上。因此，我们使用两个指标来表征提示中的信息内容，并评估低质量样本驱动树木之间冲突的程度。可以使用这些指标客观地识别物种树中拓扑冲突和非单系属的大多数实例。对于基于尖端的过滤后仍然不一致的子分支，我们使用机器学习方法来确定系统发育信号或噪声是否是支持替代拓扑的指标的更重要预测因子。我们发现，当信号支持其中一种拓扑时，噪声是支持替代拓扑的性能不佳模型中最重要的变量。总之，我们展示了基因树不一致的人为来源，这在许多数据集中可能是一种普遍现象，