Museum of Zoology and Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.
Syst Biol. 2020 Nov 1;69(6):1200-1211. doi: 10.1093/sysbio/syaa031.
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 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 predetermined 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. [Comparative methods; Dirichlet multinomial; ecological niche evolution; macroevolution; Markov model.].
复杂生态特征的进化动态——包括饮食、栖息地和行为的多态表示——仍然知之甚少。由于其多维性质，重建涉及此类特征的转变的节奏、模式和历史序列对比较生物学家来说构成了许多挑战。连续时间马尔可夫链通常用于在系统发育树上模拟生态位进化，但受到分类群是单态的和状态是单变量分类变量的假设的限制。因此，分析许多复杂特征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，但该过程会导致信息扭曲和丢失。这种方法还将状态分配的解释与抽样变化的影响混淆，因为它没有将个体物种的经验观察值直接纳入统计推断模型。在这项研究中，我们开发了一种 Dirichlet 多项式框架来在系统发育树上模拟资源利用的进化。我们的方法专门用于对多维生态特征进行建模，并考虑了由于抽样变化的影响而导致终端分类群状态分配的不确定性。该方法使用跨一系列离散资源类别对个体物种进行采样的多元计数数据，同时推断生态状态的数量、不同状态对不同资源的比例利用以及生态状态在现存物种及其祖先中的系统发育分布。该方法具有通用性，可以应用于任何可以表示为不同类别观测计数集的数据。 [比较方法；Dirichlet 多项式；生态位进化；宏观进化；马尔可夫模型。]。
