The evolutionary dynamics of complex ecological traits - including multistate representations of diet, habitat, and behavior - remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data across a set of discrete resource categories sampled for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.

中文翻译：

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系统发育树上的复杂生态表型：多变量计数数据比较分析的马尔可夫过程模型

复杂生态特征的进化动力学——包括饮食、栖息地和行为的多态表征——仍然知之甚少。由于其多维性质，重建涉及这些特征的转变的节奏、模式和历史序列对比较生物学家提出了许多挑战。连续时间马尔可夫链 (CTMC) 通常用于模拟系统发育树上的生态位进化，但受限于分类群是单态的和状态是单变量分类变量的假设。因此，分析许多复杂性状的必要第一步是将物种分类为预定数量的单变量生态状态，但此过程可能导致信息失真和丢失。这种方法还混淆了对状态分配的解释与抽样变化的影响，因为它没有直接将单个物种的经验观察纳入统计推断模型。在这项研究中，我们开发了一个狄利克雷多项式框架来模拟系统发育树上的资源利用进化。我们的方法专门设计用于对多维生态特征进行建模，并解释由采样变化影响引起的终端分类群状态分配的不确定性。该方法使用跨一组为单个物种采样的离散资源类别的多元计数数据来同时推断生态状态的数量、不同状态对不同资源的利用比例、以及生物物种及其祖先之间生态状态的系统发育分布。该方法是通用的，可以应用于任何可表示为一组来自不同类别的观察计数的数据。