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生物种间演化亲缘关系的研究热情持续高涨，这重新激发了形态演化建模统计方法的研究兴趣。当前主流统计方法均采用Mk模型(Mk model)描述离散性状(discrete character)状态间的替换过程。尽管Mk模型已是一项重要进展，但其在生物学解释层面仍存在诸多难题，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(morphological phylogenetics)领域的另一核心难题，在于离散性状的编码流程。离散性状编码往往带有主观性，其结果可能因研究者个体的主观解读而产生分歧。采用连续测量数据开展系统发育推断，或可缓解上述部分问题。尽管连续性状演化模型尚未广泛应用于系统发育拓扑结构推断，但相关模型已得到充分研究，其统计特性也已被透彻阐明。此外，连续测量数据可规避离散性状归态时常见的大量歧义问题。本研究通过一系列模拟实验，探究连续性状(continuous character)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可能性。本研究通过从模拟生成的连续与离散性状中推断系统发育关系，对比二者的重建精度差异。实验结果表明：当以无界布朗运动(Brownian motion)模拟演化过程时，连续性状的整体重建精度显著高于基于Mk模型的离散性状重建；而当以奥恩斯坦-乌伦贝克模型(Ornstein-Uhlenbeck model)模拟演化时，二者重建性能相当。上述结果证实连续性状具备可观的应用前景。当性状位点间存在协方差时，连续性状仍能保持较为出色的重建性能。本研究认为，相较于多数离散化方案，连续性状可保留状态空间中更多的变异信息，因此直接基于连续性状推断系统发育关系，或有助于最大化形态数据集所蕴含的系统发育信息。本研究同时提出，在系统发育重建中采用连续性状模型，或可规避离散性状模型适用性相关的潜在争议，同时也明确了该领域有待进一步探究的方向。本研究首次通过可控实验，验证了连续性状应用于系统发育推断的有效性。