Models of trait evolution form an important part of macroevolutionary biology. The Brownian motion model and Ornstein-Uhlenbeck models have become classic (null) models of character evolution, in which species evolve independently. Recently, models incorporating species interactions have been developed, particularly involving competition where abiotic factors pull species toward an optimal trait value and competitive interactions drive the trait values apart. However, these models assume a fitness function rather than derive it from population dynamics and they do not consider dynamics of the trait variance. Here we develop a general coherent trait evolution framework where the fitness function is based on a model of population dynamics, and therefore it can, in principle, accommodate any type of species interaction. We illustrate our framework with a model of abundance-dependent competitive interactions against a macroevolutionary background encoded in a phylogenetic tree. We develop an inference tool based on Approximate Bayesian Computation and test it on simulated data (of traits at the tips). We find that inference performs well when the diversity predicted by the parameters equals the number of species in the phylogeny. We then fit the model to empirical data of baleen whale body lengths, using three different summary statistics, and compare it to a model without population dynamics and a model where competition depends on the total metabolic rate of the competitors. We show that the unweighted model performs best for the least informative summary statistic, while the model with competition weighted by the total metabolic rate fits the data slightly better than the other two models for the two more informative summary statistics. Regardless of the summary statistic used, the three models substantially differ in their predictions of the abundance distribution. Therefore, data on abundance distributions will allow us to better distinguish the models from one another, and infer the nature of species interactions. Thus our framework provides a conceptual approach to reveal species interactions underlying trait evolution and identifies the data needed to do so in practice.

中文翻译：

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推断物种相互作用对性状进化的影响

性状进化模型是宏观进化生物学的重要组成部分。Brownian 运动模型和 Ornstein-Uhlenbeck 模型已成为角色进化的经典（空）模型，其中物种独立进化。最近，已经开发了包含物种相互作用的模型，特别是涉及竞争，其中非生物因素将物种拉向最佳性状值，而竞争性相互作用将性状值分开。然而，这些模型假设一个适应度函数而不是从种群动态中推导出它，并且它们没有考虑性状方差的动态。在这里，我们开发了一个通用的连贯性状进化框架，其中适应度函数基于种群动态模型，因此原则上它可以适应任何类型的物种相互作用。我们用一个依赖于丰度的竞争相互作用模型来说明我们的框架，该模型与系统发育树中编码的宏观进化背景相反。我们开发了一种基于近似贝叶斯计算的推理工具，并在模拟数据（提示特征）上对其进行测试。我们发现当参数预测的多样性等于系统发育中的物种数量时，推理效果很好。然后，我们使用三种不同的汇总统计将模型拟合到须鲸身体长度的经验数据，并将其与没有种群动态的模型和竞争取决于竞争者的总代谢率的模型进行比较。我们表明未加权模型对于信息量最少的汇总统计量表现最佳，而以总代谢率加权的竞争模型对数据的拟合略好于其他两个模型，用于两个信息量更大的汇总统计数据。无论使用何种汇总统计量，这三个模型在对丰度分布的预测方面存在很大差异。因此，丰度分布的数据将使我们能够更好地区分模型，并推断物种相互作用的性质。因此，我们的框架提供了一种概念方法来揭示特征进化背后的物种相互作用，并确定在实践中这样做所需的数据。丰度分布数据将使我们能够更好地将模型彼此区分开来，并推断物种相互作用的性质。因此，我们的框架提供了一种概念方法来揭示特征进化背后的物种相互作用，并确定在实践中这样做所需的数据。丰度分布数据将使我们能够更好地将模型彼此区分开来，并推断物种相互作用的性质。因此，我们的框架提供了一种概念方法来揭示特征进化背后的物种相互作用，并确定在实践中这样做所需的数据。