Data from: Use of continuous traits can improve morphological phylogenetics

The recent surge in enthusiasm for simultaneously inferring relationships from extinct and extant species has reinvigorated interest in statistical approaches for modelling morphological evolution. Current statistical methods use the Mk model to describe substitutions between discrete character states. Although representing a significant step forward, the Mk model presents challenges in biological interpretation, and its adequacy in modelling morphological evolution has not been well explored. Another major hurdle in morphological phylogenetics concerns the process of character coding of discrete characters. The often subjective nature of discrete character coding can generate discordant results that are rooted in individual researchers' subjective interpretations. Employing continuous measurements to infer phylogenies may alleviate some of these issues. Although not widely used in the inference of topology, models describing the evolution of continuous characters have been well examined, and their statistical behaviour is well understood. Also, continuous measurements avoid the substantial ambiguity often associated with the assignment of discrete characters to states. I present a set of simulations to determine whether use of continuous characters is a feasible alternative or supplement to discrete characters for inferring phylogeny. I compare relative reconstruction accuracy by inferring phylogenies from simulated continuous and discrete characters. These tests demonstrate significant promise for continuous traits by demonstrating their higher overall accuracy as compared to reconstruction from discrete characters under Mk when simulated under unbounded Brownian motion, and equal performance when simulated under an Ornstein-Uhlenbeck model. Continuous characters also perform reasonably well in the presence of covariance between sites. I argue that inferring phylogenies directly from continuous traits may be benefit efforts to maximise phylogenetic information in morphological datasets by preserving larger variation in state space compared to many discretisation schemes. I also suggest that the use of continuous trait models in phylogenetic reconstruction may alleviate potential concerns of discrete character model adequacy, while identifying areas that require further study in this area. This study provides an initial controlled demonstration of the efficacy of continuous characters in phylogenetic inference.

近年来，学界对同时推断灭绝与现存物种间演化关系的热情高涨，重新激发了形态演化建模统计方法的研究兴趣。当前主流统计方法采用Mk模型（Mk model）描述离散性状状态间的替换过程。尽管该模型已是重要的研究进展，但Mk模型在生物学解释层面存在局限，且其在形态演化建模中的适用性尚未得到充分探究。形态系统发育学领域的另一大核心障碍在于离散性状的编码流程：离散性状编码往往带有主观性，不同研究者的主观解读可能导致结果出现分歧。采用连续测量数据推断系统发育，或可缓解上述部分问题。尽管该类方法在系统发育拓扑推断中尚未得到广泛应用，但描述连续性状演化的模型已得到充分研究，其统计特性也已被深入阐明。此外，连续测量数据可规避离散性状归态时常见的显著歧义。本研究开展了一系列模拟实验，旨在探究连续性状作为离散性状的可行替代或补充方案，用于系统发育推断的合理性。通过对模拟生成的连续与离散性状数据进行系统发育推断，本研究对比了二者的重建准确率。实验结果显示，当模拟数据遵循无界布朗运动模型时，基于连续性状的重建整体准确率显著高于基于Mk模型的离散性状重建；而当模拟数据遵循奥恩斯坦-乌伦贝克模型（Ornstein-Uhlenbeck model）时，二者性能相当，这充分展现了连续性状的应用潜力。当性状间存在协方差时，连续性状仍能保持较为优异的表现。本研究认为，相较于多数离散化方案，连续性状可保留更大的状态空间变异，因此直接基于连续性状推断系统发育，或有助于最大化形态数据集的系统发育信息含量。同时，本研究提出，在系统发育重建中采用连续性状模型，或可缓解学界对离散性状模型适用性的潜在担忧，并为该领域指明了待进一步探索的方向。本研究首次通过可控实验，证实了连续性状在系统发育推断中的应用有效性。