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关系的热情高涨，这重新激发了研究者对形态演化建模统计方法的关注。当前的统计方法多采用Mk模型（Mk model）来描述离散性状状态间的替换过程。尽管该模型已是一项重要进展，但Mk模型在生物学解释层面存在局限，且其用于形态演化建模的适用性尚未得到充分探究。形态系统发育学中另一大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，这会因研究者个体的主观解读而产生不一致的研究结果。采用连续测量数据进行系统发育推断，或可缓解上述部分问题。尽管这类方法在拓扑结构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，其统计特性也已被透彻理解；此外，连续测量数据可避免离散性状状态赋值时常见的大量歧义问题。本研究开展了一系列模拟实验，以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可能性。本研究通过对模拟生成的连续与离散性状进行系统发育推断，比较二者的重建精度相对优劣。相关测试结果显示，连续性状展现出极大的应用潜力：当模拟数据基于无界布朗运动生成时，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结果；而当模拟数据基于奥恩斯坦-乌伦贝克模型（Ornstein-Uhlenbeck model）生成时，二者的重建性能不相上下。当存在位点间协方差时，连续性状同样表现出色。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进行系统发育推断，有助于最大化形态数据集内的系统发育信息。此外，本研究提出，在系统发育重建中采用连续性状模型，或可缓解离散性状模型适用性方面的潜在争议，同时也指出了该领域仍需进一步探究的方向。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有效性。